diff --git a/acl_miniconf/data.py b/acl_miniconf/data.py index 913b15b3e..1dc40f1f6 100644 --- a/acl_miniconf/data.py +++ b/acl_miniconf/data.py @@ -217,6 +217,7 @@ class Paper(BaseModel): abstract: str tldr: str keywords: List[str] = [] + languages: List[str] = [] underline_url: Optional[str] = None underline_id: Optional[int] = None preview_image: Optional[str] = None diff --git a/acl_miniconf/import_acl2023.py b/acl_miniconf/import_acl2023.py index d4e5967f3..b73e2336e 100644 --- a/acl_miniconf/import_acl2023.py +++ b/acl_miniconf/import_acl2023.py @@ -144,6 +144,13 @@ class AnthologyEntry(BaseModel): authors: List[AnthologyAuthor] = [] +class Keywords(BaseModel): + paper_id: str + track: str + keywords: List[str] = [] + languages: List[str] = [] + + def to_anthology_id(paper_id: str): if paper_id.startswith("P"): return paper_id[1:] @@ -209,6 +216,7 @@ def __init__( workshops_yaml_path: Path, booklet_json_path: Path, socials_json_path: Path, + keywords_csv_path: Path, acl_anthology_prefix: str, ): self.poster_tsv_path = poster_tsv_path @@ -226,6 +234,7 @@ def __init__( self.workshops_yaml_path = workshops_yaml_path self.booklet_json_path = booklet_json_path self.socials_json_path = socials_json_path + self.keywords_csv_path = keywords_csv_path self.acl_anthology_prefix = acl_anthology_prefix self.booklet: Booklet = Booklet.from_booklet_data( booklet_json_path, workshops_yaml_path @@ -239,6 +248,7 @@ def __init__( self.underline_assets: Dict[str, Assets] = {} self.zone = pytz.timezone("America/Toronto") self.workshops: Dict[str, Workshop] = {} + self.keywords: Dict[str, Keywords] = {} self.spreadsheet_info: Dict = {} def parse(self): @@ -247,6 +257,7 @@ def parse(self): # Underline has to be parsed early to fill in links/files/etc self._parse_underline_assets() self._parse_underline_spreadsheet() + self._parse_keywords() # Early parse special sessions, so they can be filled in self._parse_workshops() self._parse_plenaries() @@ -304,6 +315,29 @@ def get_anthology_urls(self, paper_type: str, paper_length: str, anthology_publi anthology_url = self.acl_anthology_prefix + f"2023.acl-{paper_length}.{anthology_publication_id}" paper_pdf = self.acl_anthology_prefix + f"2023.acl-{paper_length}.{anthology_publication_id}.pdf" return anthology_url, paper_pdf + + def _parse_keywords(self): + df = pd.read_csv('data/acl_2023/data/keywords.csv', sep=',').fillna("") + for _, r in df.iterrows(): + submission_id = r['Submission ID'] + paper_id = f'P{submission_id}' + track = r['Track'] + assert len(track) != 0 + if r['Keywords'] == "": + keywords = [] + else: + keywords = r['Keywords'].split("|") + if r['Languages'] == "": + languages = [] + else: + languages = r['Languages'].split("|") + + self.keywords[paper_id] = Keywords( + paper_id=paper_id, + track=track, + keywords=keywords, + languages=languages, + ) def _parse_tutorials(self): self.tutorials = self.booklet.tutorials @@ -567,6 +601,14 @@ def _parse_spotlight_papers(self): anthology_url = None paper_pdf = None + if paper_id in self.keywords: + kw = self.keywords[paper_id] + keywords = kw.keywords + languages = kw.languages + else: + keywords = [] + languages = [] + paper = Paper( id=paper_id, program=determine_program(row.Category), @@ -579,6 +621,8 @@ def _parse_spotlight_papers(self): paper_type=paper_type, category=row.Category, abstract=abstract, + keywords=keywords, + languages=languages, tldr=tldr, event_ids=[event.id], underline_id=assets.underline_id, @@ -686,6 +730,14 @@ def _parse_virtual_papers(self): anthology_url = None paper_pdf = None + if paper_id in self.keywords: + kw = self.keywords[paper_id] + keywords = kw.keywords + languages = kw.languages + else: + keywords = [] + languages = [] + paper = Paper( id=paper_id, program=determine_program(row.Category), @@ -698,6 +750,8 @@ def _parse_virtual_papers(self): paper_type=paper_type, category=row.Category, abstract=abstract, + keywords=keywords, + languages=languages, tldr=tldr, event_ids=[event.id], similar_paper_ids=[], @@ -808,6 +862,14 @@ def _parse_poster_papers(self): anthology_url = None paper_pdf = None + if paper_id in self.keywords: + kw = self.keywords[paper_id] + keywords = kw.keywords + languages = kw.languages + else: + keywords = [] + languages = [] + paper = Paper( id=paper_id, program=determine_program(row.Category), @@ -820,6 +882,8 @@ def _parse_poster_papers(self): paper_type=paper_type, category=row.Category, abstract=abstract, + languages=languages, + keywords=keywords, tldr=tldr, event_ids=[event.id], underline_id=assets.underline_id, @@ -917,6 +981,14 @@ def _parse_oral_papers(self): tldr = "" anthology_url = None paper_pdf = None + + if paper_id in self.keywords: + kw = self.keywords[paper_id] + keywords = kw.keywords + languages = kw.languages + else: + keywords = [] + languages = [] paper = Paper( id=paper_id, @@ -930,6 +1002,8 @@ def _parse_oral_papers(self): paper_type=paper_type, category=row.Category, abstract=abstract, + keywords=keywords, + languages=languages, tldr=tldr, event_ids=[event.id], underline_id=assets.underline_id, @@ -1229,6 +1303,7 @@ def main( workshops_yaml: str = "data/acl_2023/data/workshops.yaml", booklet_json: str = "data/acl_2023/data/booklet_data.json", socials_json: str = "data/acl_2023/data/socials_data.json", + keywords_csv: str = "data/acl_2023/data/keywords.csv", acl_anthology_prefix: str = "https://aclanthology.org/", out_dir: str = "data/acl_2023/data/", ): @@ -1248,6 +1323,7 @@ def main( workshops_yaml_path=Path(workshops_yaml), booklet_json_path=Path(booklet_json), socials_json_path=Path(socials_json), + keywords_csv_path=Path(keywords_csv), acl_anthology_prefix=acl_anthology_prefix, ) conf = parser.parse() diff --git a/data/acl_2023/data/conference.json b/data/acl_2023/data/conference.json index 623506608..4ae9ecce3 100644 --- a/data/acl_2023/data/conference.json +++ b/data/acl_2023/data/conference.json @@ -6691,6 +6691,7 @@ "id": "ACL-CODI_1", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "all", @@ -6723,6 +6724,7 @@ "id": "ACL-CODI_2", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "all", @@ -6753,6 +6755,7 @@ "id": "ACL-CODI_3", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "relations", @@ -6789,6 +6792,7 @@ "id": "ACL-CODI_4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "all", @@ -6823,6 +6827,7 @@ "id": "ACL_1", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -6856,6 +6861,7 @@ "id": "ACL_11", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -6890,6 +6896,7 @@ "id": "ACL_12", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -6923,6 +6930,7 @@ "id": "ACL_1282", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -6954,6 +6962,7 @@ "id": "ACL_13", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -6987,6 +6996,7 @@ "id": "ACL_1380", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -7022,6 +7032,7 @@ "id": "ACL_14", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -7054,6 +7065,7 @@ "id": "ACL_1454", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -7087,6 +7099,7 @@ "id": "ACL_15", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -7120,6 +7133,7 @@ "id": "ACL_16", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -7151,6 +7165,7 @@ "id": "ACL_17", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -7184,6 +7199,7 @@ "id": "ACL_18", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -7215,6 +7231,7 @@ "id": "ACL_19", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Non-Archival", @@ -7247,6 +7264,7 @@ "id": "ACL_2", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short Paper", @@ -7277,6 +7295,7 @@ "id": "ACL_21", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -7310,6 +7329,7 @@ "id": "ACL_22", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short Paper", @@ -7343,6 +7363,7 @@ "id": "ACL_23", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -7377,6 +7398,7 @@ "id": "ACL_24", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -7409,6 +7431,7 @@ "id": "ACL_25", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -7443,6 +7466,7 @@ "id": "ACL_2575", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -7474,6 +7498,7 @@ "id": "ACL_26", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -7505,6 +7530,7 @@ "id": "ACL_27", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -7535,6 +7561,7 @@ "id": "ACL_28", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -7567,6 +7594,7 @@ "id": "ACL_29", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -7598,6 +7626,7 @@ "id": "ACL_3", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -7629,6 +7658,7 @@ "id": "ACL_30", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Non-Archival", @@ -7662,6 +7692,7 @@ "id": "ACL_3114", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -7693,6 +7724,7 @@ "id": "ACL_32", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -7727,6 +7759,7 @@ "id": "ACL_33", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Non-Archival", @@ -7760,6 +7793,7 @@ "id": "ACL_34", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -7791,6 +7825,7 @@ "id": "ACL_35", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -7822,6 +7857,7 @@ "id": "ACL_3566", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -7858,6 +7894,7 @@ "id": "ACL_36", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long Paper", @@ -7891,6 +7928,7 @@ "id": "ACL_38", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -7923,6 +7961,7 @@ "id": "ACL_39", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -7956,6 +7995,7 @@ "id": "ACL_4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -7989,6 +8029,7 @@ "id": "ACL_40", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -8023,6 +8064,7 @@ "id": "ACL_41", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -8057,6 +8099,7 @@ "id": "ACL_4158", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -8091,6 +8134,7 @@ "id": "ACL_42", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -8127,6 +8171,7 @@ "id": "ACL_43", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -8160,6 +8205,7 @@ "id": "ACL_44", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long Paper", @@ -8193,6 +8239,7 @@ "id": "ACL_45", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Non-Archival", @@ -8224,6 +8271,7 @@ "id": "ACL_4560", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -8257,6 +8305,7 @@ "id": "ACL_46", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -8289,6 +8338,7 @@ "id": "ACL_47", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -8321,6 +8371,7 @@ "id": "ACL_48", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Shared Task System Description", @@ -8360,6 +8411,7 @@ "id": "ACL_49", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Previously Presented Work", @@ -8391,6 +8443,7 @@ "id": "ACL_5", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -8425,6 +8478,7 @@ "id": "ACL_50", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -8458,6 +8512,7 @@ "id": "ACL_51", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -8491,6 +8546,7 @@ "id": "ACL_52", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Shared Task System Description", @@ -8524,6 +8580,7 @@ "id": "ACL_53", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Non-Archival", @@ -8556,6 +8613,7 @@ "id": "ACL_54", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Non-Archival", @@ -8592,6 +8650,7 @@ "id": "ACL_55", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -8634,6 +8693,7 @@ "id": "ACL_56", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long Paper", @@ -8665,6 +8725,7 @@ "id": "ACL_57", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -8701,6 +8762,7 @@ "id": "ACL_58", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -8732,6 +8794,7 @@ "id": "ACL_59", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -8763,6 +8826,7 @@ "id": "ACL_6", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -8796,6 +8860,7 @@ "id": "ACL_61", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -8830,6 +8895,7 @@ "id": "ACL_62", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -8865,6 +8931,7 @@ "id": "ACL_65", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -8899,6 +8966,7 @@ "id": "ACL_66", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -8930,6 +8998,7 @@ "id": "ACL_67", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -8962,6 +9031,7 @@ "id": "ACL_68", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -8996,6 +9066,7 @@ "id": "ACL_7", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -9029,6 +9100,7 @@ "id": "ACL_71", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9064,6 +9136,7 @@ "id": "ACL_72", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9096,6 +9169,7 @@ "id": "ACL_73", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9129,6 +9203,7 @@ "id": "ACL_74", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9165,6 +9240,7 @@ "id": "ACL_75", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9196,6 +9272,7 @@ "id": "ACL_76", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9228,6 +9305,7 @@ "id": "ACL_77", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9266,6 +9344,7 @@ "id": "ACL_78", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9298,6 +9377,7 @@ "id": "ACL_79", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9331,6 +9411,7 @@ "id": "ACL_80", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9366,6 +9447,7 @@ "id": "ACL_82", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9405,6 +9487,7 @@ "id": "ACL_83", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9438,6 +9521,7 @@ "id": "ACL_87", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9471,6 +9555,7 @@ "id": "ACL_90", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9504,6 +9589,7 @@ "id": "ACL_91", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9537,6 +9623,7 @@ "id": "ACL_92", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9571,6 +9658,7 @@ "id": "ACL_93", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9605,6 +9693,7 @@ "id": "ACL_95", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9639,6 +9728,7 @@ "id": "ACL_97", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9678,6 +9768,7 @@ "id": "ACL_98", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9713,6 +9804,7 @@ "id": "ACL_99", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -9746,6 +9838,7 @@ "id": "ACL_F1", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Findings", @@ -9777,6 +9870,7 @@ "id": "ACL_F10", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Findings", @@ -9813,6 +9907,7 @@ "id": "ACL_F11", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Findings", @@ -9845,6 +9940,7 @@ "id": "ACL_F12", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Findings", @@ -9876,6 +9972,7 @@ "id": "ACL_F2", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Findings", @@ -9910,6 +10007,7 @@ "id": "ACL_F3", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Findings", @@ -9945,6 +10043,7 @@ "id": "ACL_F4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Findings", @@ -9977,6 +10076,7 @@ "id": "ACL_F5", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Findings", @@ -10009,6 +10109,7 @@ "id": "ACL_F6", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Findings", @@ -10043,6 +10144,7 @@ "id": "ACL_F7", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Findings", @@ -10074,6 +10176,7 @@ "id": "ACL_F8", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Findings", @@ -10106,6 +10209,7 @@ "id": "ACL_F9", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Findings", @@ -10145,6 +10249,7 @@ "id": "ACL_G2P", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10175,6 +10280,7 @@ "id": "ACL_G2P1", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10205,6 +10311,7 @@ "id": "ACL_G2P2", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10235,6 +10342,7 @@ "id": "ACL_G2P3", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10265,6 +10373,7 @@ "id": "ACL_G2P4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10301,6 +10410,7 @@ "id": "ACL_Gloss", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10337,6 +10447,7 @@ "id": "ACL_Inf1", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10368,6 +10479,7 @@ "id": "ACL_Inf2", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10407,6 +10519,7 @@ "id": "BEA_10", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10438,6 +10551,7 @@ "id": "BEA_100", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10471,6 +10585,7 @@ "id": "BEA_102", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10506,6 +10621,7 @@ "id": "BEA_106", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10539,6 +10655,7 @@ "id": "BEA_11", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10571,6 +10688,7 @@ "id": "BEA_110", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10603,6 +10721,7 @@ "id": "BEA_111", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10636,6 +10755,7 @@ "id": "BEA_112", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10669,6 +10789,7 @@ "id": "BEA_113", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10700,6 +10821,7 @@ "id": "BEA_114", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10733,6 +10855,7 @@ "id": "BEA_115", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10767,6 +10890,7 @@ "id": "BEA_116", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10798,6 +10922,7 @@ "id": "BEA_117", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10836,6 +10961,7 @@ "id": "BEA_14", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10868,6 +10994,7 @@ "id": "BEA_16", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10901,6 +11028,7 @@ "id": "BEA_18", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10933,6 +11061,7 @@ "id": "BEA_19", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -10965,6 +11094,7 @@ "id": "BEA_20", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11003,6 +11133,7 @@ "id": "BEA_21", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11039,6 +11170,7 @@ "id": "BEA_24", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11072,6 +11204,7 @@ "id": "BEA_25", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11104,6 +11237,7 @@ "id": "BEA_28", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11135,6 +11269,7 @@ "id": "BEA_3", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11168,6 +11303,7 @@ "id": "BEA_30", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11199,6 +11335,7 @@ "id": "BEA_32", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11231,6 +11368,7 @@ "id": "BEA_33", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11263,6 +11401,7 @@ "id": "BEA_34", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11296,6 +11435,7 @@ "id": "BEA_37", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11331,6 +11471,7 @@ "id": "BEA_39", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11363,6 +11504,7 @@ "id": "BEA_4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11397,6 +11539,7 @@ "id": "BEA_40", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11429,6 +11572,7 @@ "id": "BEA_42", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11462,6 +11606,7 @@ "id": "BEA_44", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11495,6 +11640,7 @@ "id": "BEA_45", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11527,6 +11673,7 @@ "id": "BEA_47", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11560,6 +11707,7 @@ "id": "BEA_48", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11592,6 +11740,7 @@ "id": "BEA_49", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11631,6 +11780,7 @@ "id": "BEA_50", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11663,6 +11813,7 @@ "id": "BEA_55", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11697,6 +11848,7 @@ "id": "BEA_56", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11728,6 +11880,7 @@ "id": "BEA_57", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11759,6 +11912,7 @@ "id": "BEA_58", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11798,6 +11952,7 @@ "id": "BEA_6", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11833,6 +11988,7 @@ "id": "BEA_61", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11864,6 +12020,7 @@ "id": "BEA_62", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11894,6 +12051,7 @@ "id": "BEA_63", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11930,6 +12088,7 @@ "id": "BEA_65", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11964,6 +12123,7 @@ "id": "BEA_66", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -11995,6 +12155,7 @@ "id": "BEA_67", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -12027,6 +12188,7 @@ "id": "BEA_69", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -12058,6 +12220,7 @@ "id": "BEA_7", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -12089,6 +12252,7 @@ "id": "BEA_74", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -12120,6 +12284,7 @@ "id": "BEA_75", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -12155,6 +12320,7 @@ "id": "BEA_76", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -12188,6 +12354,7 @@ "id": "BEA_77", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -12221,6 +12388,7 @@ "id": "BEA_78", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -12255,6 +12423,7 @@ "id": "BEA_8", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -12288,6 +12457,7 @@ "id": "BEA_81", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -12321,6 +12491,7 @@ "id": "BEA_86", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -12353,6 +12524,7 @@ "id": "BEA_88", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -12384,6 +12556,7 @@ "id": "BEA_9", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -12418,6 +12591,7 @@ "id": "BEA_90", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -12452,6 +12626,7 @@ "id": "BEA_93", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -12483,6 +12658,7 @@ "id": "BEA_97", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -12518,6 +12694,7 @@ "id": "BEA_99", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -12550,6 +12727,7 @@ "id": "BioNLP_1", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long", @@ -12580,6 +12758,7 @@ "id": "BioNLP_101", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -12613,6 +12792,7 @@ "id": "BioNLP_102", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -12645,6 +12825,7 @@ "id": "BioNLP_104", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -12684,6 +12865,7 @@ "id": "BioNLP_106", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -12717,6 +12899,7 @@ "id": "BioNLP_107", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -12752,6 +12935,7 @@ "id": "BioNLP_108", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -12783,6 +12967,7 @@ "id": "BioNLP_11", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -12815,6 +13000,7 @@ "id": "BioNLP_110", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -12848,6 +13034,7 @@ "id": "BioNLP_111", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -12881,6 +13068,7 @@ "id": "BioNLP_112", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -12913,6 +13101,7 @@ "id": "BioNLP_113", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -12945,6 +13134,7 @@ "id": "BioNLP_114", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -12978,6 +13168,7 @@ "id": "BioNLP_116", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -13010,6 +13201,7 @@ "id": "BioNLP_117", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -13049,6 +13241,7 @@ "id": "BioNLP_118", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -13081,6 +13274,7 @@ "id": "BioNLP_12", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -13113,6 +13307,7 @@ "id": "BioNLP_120", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -13146,6 +13341,7 @@ "id": "BioNLP_121", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -13178,6 +13374,7 @@ "id": "BioNLP_122", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -13210,6 +13407,7 @@ "id": "BioNLP_123", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -13242,6 +13440,7 @@ "id": "BioNLP_124", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -13274,6 +13473,7 @@ "id": "BioNLP_125", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -13306,6 +13506,7 @@ "id": "BioNLP_126", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -13339,6 +13540,7 @@ "id": "BioNLP_127", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -13372,6 +13574,7 @@ "id": "BioNLP_128", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -13404,6 +13607,7 @@ "id": "BioNLP_13", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -13435,6 +13639,7 @@ "id": "BioNLP_130", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -13467,6 +13672,7 @@ "id": "BioNLP_14", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -13499,6 +13705,7 @@ "id": "BioNLP_15", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -13534,6 +13741,7 @@ "id": "BioNLP_16", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -13565,6 +13773,7 @@ "id": "BioNLP_17", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -13596,6 +13805,7 @@ "id": "BioNLP_18", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -13628,6 +13838,7 @@ "id": "BioNLP_19", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -13661,6 +13872,7 @@ "id": "BioNLP_2", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long", @@ -13693,6 +13905,7 @@ "id": "BioNLP_20", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -13726,6 +13939,7 @@ "id": "BioNLP_22", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -13758,6 +13972,7 @@ "id": "BioNLP_24", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -13792,6 +14007,7 @@ "id": "BioNLP_25", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -13827,6 +14043,7 @@ "id": "BioNLP_28", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -13859,6 +14076,7 @@ "id": "BioNLP_29", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -13891,6 +14109,7 @@ "id": "BioNLP_31", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -13927,6 +14146,7 @@ "id": "BioNLP_32", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -13959,6 +14179,7 @@ "id": "BioNLP_34", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -13991,6 +14212,7 @@ "id": "BioNLP_35", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -14022,6 +14244,7 @@ "id": "BioNLP_40", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -14056,6 +14279,7 @@ "id": "BioNLP_41", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -14089,6 +14313,7 @@ "id": "BioNLP_42", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -14122,6 +14347,7 @@ "id": "BioNLP_43", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -14160,6 +14386,7 @@ "id": "BioNLP_44", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -14193,6 +14420,7 @@ "id": "BioNLP_46", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -14224,6 +14452,7 @@ "id": "BioNLP_47", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -14256,6 +14485,7 @@ "id": "BioNLP_48", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -14288,6 +14518,7 @@ "id": "BioNLP_49", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -14320,6 +14551,7 @@ "id": "BioNLP_5", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long", @@ -14351,6 +14583,7 @@ "id": "BioNLP_50", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -14383,6 +14616,7 @@ "id": "BioNLP_51", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -14415,6 +14649,7 @@ "id": "BioNLP_53", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -14449,6 +14684,7 @@ "id": "BioNLP_55", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -14483,6 +14719,7 @@ "id": "BioNLP_56", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -14515,6 +14752,7 @@ "id": "BioNLP_57", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -14548,6 +14786,7 @@ "id": "BioNLP_58", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -14582,6 +14821,7 @@ "id": "BioNLP_59", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -14623,6 +14863,7 @@ "id": "BioNLP_61", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -14656,6 +14897,7 @@ "id": "BioNLP_62", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -14692,6 +14934,7 @@ "id": "BioNLP_63", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -14725,6 +14968,7 @@ "id": "BioNLP_64", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short paper", @@ -14762,6 +15006,7 @@ "id": "BioNLP_7", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -14800,6 +15045,7 @@ "id": "BioNLP_9", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long paper", @@ -14834,6 +15080,7 @@ "id": "C2092", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "CL", @@ -14867,6 +15114,7 @@ "id": "C2121", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "CL", @@ -14900,6 +15148,7 @@ "id": "C2147", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "CL", @@ -14931,6 +15180,7 @@ "id": "C2208", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "CL", @@ -14965,6 +15215,7 @@ "id": "C2217", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "CL", @@ -14997,6 +15248,7 @@ "id": "C2265", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "CL", @@ -15029,6 +15281,7 @@ "id": "C2281", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "CL", @@ -15059,6 +15312,7 @@ "id": "CAWL_10", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -15089,6 +15343,7 @@ "id": "CAWL_12", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -15121,6 +15376,7 @@ "id": "CAWL_13", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -15155,6 +15411,7 @@ "id": "CAWL_14", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -15185,6 +15442,7 @@ "id": "CAWL_18", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -15216,6 +15474,7 @@ "id": "CAWL_20", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -15248,6 +15507,7 @@ "id": "CAWL_4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -15278,6 +15538,7 @@ "id": "CAWL_5", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -15311,6 +15572,7 @@ "id": "CAWL_6", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -15344,6 +15606,7 @@ "id": "CAWL_7", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -15376,6 +15639,7 @@ "id": "CAWL_8", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -15408,6 +15672,7 @@ "id": "CAWL_9", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -15446,6 +15711,7 @@ "id": "CODI_1", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular long", @@ -15478,6 +15744,7 @@ "id": "CODI_10", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular short", @@ -15511,6 +15778,7 @@ "id": "CODI_12", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular short", @@ -15545,6 +15813,7 @@ "id": "CODI_13", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Extended abstract", @@ -15575,6 +15844,7 @@ "id": "CODI_14", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular short", @@ -15606,6 +15876,7 @@ "id": "CODI_16", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular long", @@ -15640,6 +15911,7 @@ "id": "CODI_17", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular short", @@ -15673,6 +15945,7 @@ "id": "CODI_18", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular long", @@ -15708,6 +15981,7 @@ "id": "CODI_19", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Extended abstract", @@ -15741,6 +16015,7 @@ "id": "CODI_20", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular short", @@ -15775,6 +16050,7 @@ "id": "CODI_22", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular short", @@ -15806,6 +16082,7 @@ "id": "CODI_23", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular long", @@ -15838,6 +16115,7 @@ "id": "CODI_24", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular long", @@ -15873,6 +16151,7 @@ "id": "CODI_25", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular long", @@ -15908,6 +16187,7 @@ "id": "CODI_26", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular long", @@ -15942,6 +16222,7 @@ "id": "CODI_27", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular long", @@ -15972,6 +16253,7 @@ "id": "CODI_28", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular long", @@ -16003,6 +16285,7 @@ "id": "CODI_29", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular short", @@ -16036,6 +16319,7 @@ "id": "CODI_30", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular long", @@ -16066,6 +16350,7 @@ "id": "CODI_31", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular short", @@ -16098,6 +16383,7 @@ "id": "CODI_33", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Extended abstract", @@ -16136,6 +16422,7 @@ "id": "CODI_35", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Extended abstract", @@ -16167,6 +16454,7 @@ "id": "CODI_37", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Extended abstract", @@ -16199,6 +16487,7 @@ "id": "CODI_38", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Extended abstract", @@ -16232,6 +16521,7 @@ "id": "CODI_39", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Extended abstract", @@ -16264,6 +16554,7 @@ "id": "CODI_4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular short", @@ -16296,6 +16587,7 @@ "id": "CODI_40", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Extended abstract", @@ -16327,6 +16619,7 @@ "id": "CODI_5", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular long", @@ -16359,6 +16652,7 @@ "id": "CODI_6", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Regular long", @@ -16391,6 +16685,7 @@ "id": "ClinicalNLP_12", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16425,6 +16720,7 @@ "id": "ClinicalNLP_13", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16460,6 +16756,7 @@ "id": "ClinicalNLP_16", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16493,6 +16790,7 @@ "id": "ClinicalNLP_19", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16528,6 +16826,7 @@ "id": "ClinicalNLP_2", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16562,6 +16861,7 @@ "id": "ClinicalNLP_20", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16593,6 +16893,7 @@ "id": "ClinicalNLP_22", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16627,6 +16928,7 @@ "id": "ClinicalNLP_26", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16659,6 +16961,7 @@ "id": "ClinicalNLP_27", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16692,6 +16995,7 @@ "id": "ClinicalNLP_3", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16726,6 +17030,7 @@ "id": "ClinicalNLP_30", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16762,6 +17067,7 @@ "id": "ClinicalNLP_31", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16798,6 +17104,7 @@ "id": "ClinicalNLP_33", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16829,6 +17136,7 @@ "id": "ClinicalNLP_35", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16863,6 +17171,7 @@ "id": "ClinicalNLP_36", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16898,6 +17207,7 @@ "id": "ClinicalNLP_37", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16932,6 +17242,7 @@ "id": "ClinicalNLP_38", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16964,6 +17275,7 @@ "id": "ClinicalNLP_39", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -16996,6 +17308,7 @@ "id": "ClinicalNLP_4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17028,6 +17341,7 @@ "id": "ClinicalNLP_40", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17063,6 +17377,7 @@ "id": "ClinicalNLP_42", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17095,6 +17410,7 @@ "id": "ClinicalNLP_43", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17128,6 +17444,7 @@ "id": "ClinicalNLP_45", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17160,6 +17477,7 @@ "id": "ClinicalNLP_48", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17193,6 +17511,7 @@ "id": "ClinicalNLP_49", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17228,6 +17547,7 @@ "id": "ClinicalNLP_5", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17266,6 +17586,7 @@ "id": "ClinicalNLP_50", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17298,6 +17619,7 @@ "id": "ClinicalNLP_51", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17333,6 +17655,7 @@ "id": "ClinicalNLP_52", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17365,6 +17688,7 @@ "id": "ClinicalNLP_54", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17398,6 +17722,7 @@ "id": "ClinicalNLP_55", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17430,6 +17755,7 @@ "id": "ClinicalNLP_56", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17466,6 +17792,7 @@ "id": "ClinicalNLP_57", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17501,6 +17828,7 @@ "id": "ClinicalNLP_58", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17535,6 +17863,7 @@ "id": "ClinicalNLP_6", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17567,6 +17896,7 @@ "id": "ClinicalNLP_62", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17599,6 +17929,7 @@ "id": "ClinicalNLP_65", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17638,6 +17969,7 @@ "id": "ClinicalNLP_66", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17674,6 +18006,7 @@ "id": "ClinicalNLP_68", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17711,6 +18044,7 @@ "id": "ClinicalNLP_69", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17743,6 +18077,7 @@ "id": "ClinicalNLP_7", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17777,6 +18112,7 @@ "id": "ClinicalNLP_70", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17808,6 +18144,7 @@ "id": "ClinicalNLP_71", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17842,6 +18179,7 @@ "id": "ClinicalNLP_72", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17875,6 +18213,7 @@ "id": "ClinicalNLP_74", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17910,6 +18249,7 @@ "id": "ClinicalNLP_75", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17942,6 +18282,7 @@ "id": "ClinicalNLP_76", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -17977,6 +18318,7 @@ "id": "ClinicalNLP_78", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -18009,6 +18351,7 @@ "id": "ClinicalNLP_79", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -18041,6 +18384,7 @@ "id": "ClinicalNLP_8", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -18076,6 +18420,7 @@ "id": "ClinicalNLP_80", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -18110,6 +18455,7 @@ "id": "ClinicalNLP_81", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -18141,6 +18487,7 @@ "id": "ClinicalNLP_83", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -18171,6 +18518,7 @@ "id": "ClinicalNLP_84", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -18204,6 +18552,7 @@ "id": "ClinicalNLP_85", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -18235,6 +18584,7 @@ "id": "ClinicalNLP_86", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -18267,6 +18617,7 @@ "id": "ClinicalNLP_87", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -18300,6 +18651,7 @@ "id": "ClinicalNLP_88", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "N/A", @@ -18332,6 +18684,7 @@ "id": "D103", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.39.pdf", "paper_type": "demo", @@ -18365,6 +18718,7 @@ "id": "D104", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.40.pdf", "paper_type": "demo", @@ -18397,6 +18751,7 @@ "id": "D105", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.41.pdf", "paper_type": "demo", @@ -18430,6 +18785,7 @@ "id": "D106", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.42.pdf", "paper_type": "demo", @@ -18468,6 +18824,7 @@ "id": "D107", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.43.pdf", "paper_type": "demo", @@ -18500,6 +18857,7 @@ "id": "D110", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.44.pdf", "paper_type": "demo", @@ -18540,6 +18898,7 @@ "id": "D113", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.45.pdf", "paper_type": "demo", @@ -18571,6 +18930,7 @@ "id": "D117", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.46.pdf", "paper_type": "demo", @@ -18607,6 +18967,7 @@ "id": "D118", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.47.pdf", "paper_type": "demo", @@ -18642,6 +19003,7 @@ "id": "D119", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.48.pdf", "paper_type": "demo", @@ -18685,6 +19047,7 @@ "id": "D123", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.49.pdf", "paper_type": "demo", @@ -18717,6 +19080,7 @@ "id": "D130", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.50.pdf", "paper_type": "demo", @@ -18751,6 +19115,7 @@ "id": "D132", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.51.pdf", "paper_type": "demo", @@ -18787,6 +19152,7 @@ "id": "D133", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.52.pdf", "paper_type": "demo", @@ -18825,6 +19191,7 @@ "id": "D134", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.53.pdf", "paper_type": "demo", @@ -18874,6 +19241,7 @@ "id": "D14", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.2.pdf", "paper_type": "demo", @@ -18911,6 +19279,7 @@ "id": "D141", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.54.pdf", "paper_type": "demo", @@ -18948,6 +19317,7 @@ "id": "D144", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.55.pdf", "paper_type": "demo", @@ -18980,6 +19350,7 @@ "id": "D147", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.56.pdf", "paper_type": "demo", @@ -19018,6 +19389,7 @@ "id": "D148", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.57.pdf", "paper_type": "demo", @@ -19058,6 +19430,7 @@ "id": "D156", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.58.pdf", "paper_type": "demo", @@ -19093,6 +19466,7 @@ "id": "D17", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.3.pdf", "paper_type": "demo", @@ -19124,6 +19498,7 @@ "id": "D18", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.4.pdf", "paper_type": "demo", @@ -19169,6 +19544,7 @@ "id": "D19", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.5.pdf", "paper_type": "demo", @@ -19202,6 +19578,7 @@ "id": "D24", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.6.pdf", "paper_type": "demo", @@ -19237,6 +19614,7 @@ "id": "D25", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.7.pdf", "paper_type": "demo", @@ -19269,6 +19647,7 @@ "id": "D26", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.8.pdf", "paper_type": "demo", @@ -19303,6 +19682,7 @@ "id": "D27", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.9.pdf", "paper_type": "demo", @@ -19337,6 +19717,7 @@ "id": "D30", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.10.pdf", "paper_type": "demo", @@ -19367,6 +19748,7 @@ "id": "D31", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.11.pdf", "paper_type": "demo", @@ -19401,6 +19783,7 @@ "id": "D32", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.12.pdf", "paper_type": "demo", @@ -19433,6 +19816,7 @@ "id": "D39", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.13.pdf", "paper_type": "demo", @@ -19465,6 +19849,7 @@ "id": "D44", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.14.pdf", "paper_type": "demo", @@ -19497,6 +19882,7 @@ "id": "D45", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.15.pdf", "paper_type": "demo", @@ -19529,6 +19915,7 @@ "id": "D46", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.16.pdf", "paper_type": "demo", @@ -19569,6 +19956,7 @@ "id": "D47", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.17.pdf", "paper_type": "demo", @@ -19608,6 +19996,7 @@ "id": "D49", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.18.pdf", "paper_type": "demo", @@ -19640,6 +20029,7 @@ "id": "D53", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.19.pdf", "paper_type": "demo", @@ -19695,6 +20085,7 @@ "id": "D55", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.20.pdf", "paper_type": "demo", @@ -19727,6 +20118,7 @@ "id": "D56", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.21.pdf", "paper_type": "demo", @@ -19758,6 +20150,7 @@ "id": "D63", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.22.pdf", "paper_type": "demo", @@ -19792,6 +20185,7 @@ "id": "D66", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.23.pdf", "paper_type": "demo", @@ -19825,6 +20219,7 @@ "id": "D69", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.24.pdf", "paper_type": "demo", @@ -19865,6 +20260,7 @@ "id": "D71", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.25.pdf", "paper_type": "demo", @@ -19901,6 +20297,7 @@ "id": "D74", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.26.pdf", "paper_type": "demo", @@ -19933,6 +20330,7 @@ "id": "D77", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.27.pdf", "paper_type": "demo", @@ -19967,6 +20365,7 @@ "id": "D78", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.28.pdf", "paper_type": "demo", @@ -20004,6 +20403,7 @@ "id": "D80", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.29.pdf", "paper_type": "demo", @@ -20035,6 +20435,7 @@ "id": "D84", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.30.pdf", "paper_type": "demo", @@ -20070,6 +20471,7 @@ "id": "D85", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.31.pdf", "paper_type": "demo", @@ -20106,6 +20508,7 @@ "id": "D89", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.32.pdf", "paper_type": "demo", @@ -20150,6 +20553,7 @@ "id": "D9", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.1.pdf", "paper_type": "demo", @@ -20184,6 +20588,7 @@ "id": "D90", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.33.pdf", "paper_type": "demo", @@ -20219,6 +20624,7 @@ "id": "D91", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.34.pdf", "paper_type": "demo", @@ -20251,6 +20657,7 @@ "id": "D93", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.35.pdf", "paper_type": "demo", @@ -20286,6 +20693,7 @@ "id": "D94", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.36.pdf", "paper_type": "demo", @@ -20318,6 +20726,7 @@ "id": "D95", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.37.pdf", "paper_type": "demo", @@ -20363,6 +20772,7 @@ "id": "D96", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-demo.38.pdf", "paper_type": "demo", @@ -20395,6 +20805,7 @@ "id": "DialDoc_10", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long -", @@ -20430,6 +20841,7 @@ "id": "DialDoc_12", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long -", @@ -20466,6 +20878,7 @@ "id": "DialDoc_14", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long -", @@ -20501,6 +20914,7 @@ "id": "DialDoc_15", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short -", @@ -20534,6 +20948,7 @@ "id": "DialDoc_18", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long -", @@ -20566,6 +20981,7 @@ "id": "DialDoc_2", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -20599,6 +21015,7 @@ "id": "DialDoc_3", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long -", @@ -20631,6 +21048,7 @@ "id": "DialDoc_4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short -", @@ -20670,6 +21088,7 @@ "id": "DialDoc_5", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short -", @@ -20705,6 +21124,7 @@ "id": "DialDoc_6", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long -", @@ -20736,6 +21156,7 @@ "id": "DialDoc_7", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short -", @@ -20771,6 +21192,7 @@ "id": "I100", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.36.pdf", "paper_type": "industry", @@ -20801,6 +21223,7 @@ "id": "I102", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.37.pdf", "paper_type": "industry", @@ -20835,6 +21258,7 @@ "id": "I104", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.38.pdf", "paper_type": "industry", @@ -20871,6 +21295,7 @@ "id": "I107", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.39.pdf", "paper_type": "industry", @@ -20905,6 +21330,7 @@ "id": "I109", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.40.pdf", "paper_type": "industry", @@ -20938,6 +21364,7 @@ "id": "I110", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.41.pdf", "paper_type": "industry", @@ -20971,6 +21398,7 @@ "id": "I111", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.42.pdf", "paper_type": "industry", @@ -21010,6 +21438,7 @@ "id": "I112", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.43.pdf", "paper_type": "industry", @@ -21045,6 +21474,7 @@ "id": "I119", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.44.pdf", "paper_type": "industry", @@ -21082,6 +21512,7 @@ "id": "I120", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.45.pdf", "paper_type": "industry", @@ -21119,6 +21550,7 @@ "id": "I124", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.46.pdf", "paper_type": "industry", @@ -21152,6 +21584,7 @@ "id": "I125", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.47.pdf", "paper_type": "industry", @@ -21186,6 +21619,7 @@ "id": "I128", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.48.pdf", "paper_type": "industry", @@ -21219,6 +21653,7 @@ "id": "I13", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.3.pdf", "paper_type": "industry", @@ -21254,6 +21689,7 @@ "id": "I131", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.49.pdf", "paper_type": "industry", @@ -21290,6 +21726,7 @@ "id": "I134", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.50.pdf", "paper_type": "industry", @@ -21324,6 +21761,7 @@ "id": "I135", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.51.pdf", "paper_type": "industry", @@ -21358,6 +21796,7 @@ "id": "I139", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.52.pdf", "paper_type": "industry", @@ -21390,6 +21829,7 @@ "id": "I14", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.4.pdf", "paper_type": "industry", @@ -21423,6 +21863,7 @@ "id": "I140", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.53.pdf", "paper_type": "industry", @@ -21458,6 +21899,7 @@ "id": "I141", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.54.pdf", "paper_type": "industry", @@ -21492,6 +21934,7 @@ "id": "I146", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.55.pdf", "paper_type": "industry", @@ -21526,6 +21969,7 @@ "id": "I148", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.56.pdf", "paper_type": "industry", @@ -21557,6 +22001,7 @@ "id": "I15", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.5.pdf", "paper_type": "industry", @@ -21592,6 +22037,7 @@ "id": "I156", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.57.pdf", "paper_type": "industry", @@ -21625,6 +22071,7 @@ "id": "I16", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.6.pdf", "paper_type": "industry", @@ -21656,6 +22103,7 @@ "id": "I17", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.7.pdf", "paper_type": "industry", @@ -21695,6 +22143,7 @@ "id": "I171", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.58.pdf", "paper_type": "industry", @@ -21731,6 +22180,7 @@ "id": "I173", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.59.pdf", "paper_type": "industry", @@ -21765,6 +22215,7 @@ "id": "I18", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.8.pdf", "paper_type": "industry", @@ -21802,6 +22253,7 @@ "id": "I186", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.60.pdf", "paper_type": "industry", @@ -21837,6 +22289,7 @@ "id": "I187", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.61.pdf", "paper_type": "industry", @@ -21868,6 +22321,7 @@ "id": "I188", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.62.pdf", "paper_type": "industry", @@ -21900,6 +22354,7 @@ "id": "I189", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.63.pdf", "paper_type": "industry", @@ -21934,6 +22389,7 @@ "id": "I191", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.64.pdf", "paper_type": "industry", @@ -21967,6 +22423,7 @@ "id": "I196", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.65.pdf", "paper_type": "industry", @@ -22000,6 +22457,7 @@ "id": "I197", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.66.pdf", "paper_type": "industry", @@ -22033,6 +22491,7 @@ "id": "I199", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.67.pdf", "paper_type": "industry", @@ -22068,6 +22527,7 @@ "id": "I201", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.68.pdf", "paper_type": "industry", @@ -22103,6 +22563,7 @@ "id": "I205", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.69.pdf", "paper_type": "industry", @@ -22137,6 +22598,7 @@ "id": "I207", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.70.pdf", "paper_type": "industry", @@ -22170,6 +22632,7 @@ "id": "I208", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.71.pdf", "paper_type": "industry", @@ -22206,6 +22669,7 @@ "id": "I213", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.72.pdf", "paper_type": "industry", @@ -22240,6 +22704,7 @@ "id": "I215", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.73.pdf", "paper_type": "industry", @@ -22276,6 +22741,7 @@ "id": "I222", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.74.pdf", "paper_type": "industry", @@ -22311,6 +22777,7 @@ "id": "I225", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.75.pdf", "paper_type": "industry", @@ -22344,6 +22811,7 @@ "id": "I227", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.76.pdf", "paper_type": "industry", @@ -22381,6 +22849,7 @@ "id": "I27", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.9.pdf", "paper_type": "industry", @@ -22413,6 +22882,7 @@ "id": "I29", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.10.pdf", "paper_type": "industry", @@ -22445,6 +22915,7 @@ "id": "I3", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.1.pdf", "paper_type": "industry", @@ -22478,6 +22949,7 @@ "id": "I30", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.11.pdf", "paper_type": "industry", @@ -22511,6 +22983,7 @@ "id": "I32", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.12.pdf", "paper_type": "industry", @@ -22547,6 +23020,7 @@ "id": "I36", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.13.pdf", "paper_type": "industry", @@ -22578,6 +23052,7 @@ "id": "I41", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.14.pdf", "paper_type": "industry", @@ -22614,6 +23089,7 @@ "id": "I42", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.15.pdf", "paper_type": "industry", @@ -22652,6 +23128,7 @@ "id": "I43", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.16.pdf", "paper_type": "industry", @@ -22686,6 +23163,7 @@ "id": "I45", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.17.pdf", "paper_type": "industry", @@ -22723,6 +23201,7 @@ "id": "I46", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.18.pdf", "paper_type": "industry", @@ -22759,6 +23238,7 @@ "id": "I47", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.19.pdf", "paper_type": "industry", @@ -22791,6 +23271,7 @@ "id": "I54", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.20.pdf", "paper_type": "industry", @@ -22826,6 +23307,7 @@ "id": "I55", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.21.pdf", "paper_type": "industry", @@ -22861,6 +23343,7 @@ "id": "I60", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.22.pdf", "paper_type": "industry", @@ -22897,6 +23380,7 @@ "id": "I65", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.23.pdf", "paper_type": "industry", @@ -22936,6 +23420,7 @@ "id": "I66", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.24.pdf", "paper_type": "industry", @@ -22968,6 +23453,7 @@ "id": "I69", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.25.pdf", "paper_type": "industry", @@ -22999,6 +23485,7 @@ "id": "I75", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.26.pdf", "paper_type": "industry", @@ -23037,6 +23524,7 @@ "id": "I77", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.27.pdf", "paper_type": "industry", @@ -23076,6 +23564,7 @@ "id": "I78", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.28.pdf", "paper_type": "industry", @@ -23112,6 +23601,7 @@ "id": "I8", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.2.pdf", "paper_type": "industry", @@ -23145,6 +23635,7 @@ "id": "I81", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.29.pdf", "paper_type": "industry", @@ -23178,6 +23669,7 @@ "id": "I83", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.30.pdf", "paper_type": "industry", @@ -23210,6 +23702,7 @@ "id": "I90", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.31.pdf", "paper_type": "industry", @@ -23242,6 +23735,7 @@ "id": "I92", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.32.pdf", "paper_type": "industry", @@ -23275,6 +23769,7 @@ "id": "I93", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.33.pdf", "paper_type": "industry", @@ -23314,6 +23809,7 @@ "id": "I94", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.34.pdf", "paper_type": "industry", @@ -23348,6 +23844,7 @@ "id": "I96", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-industry.35.pdf", "paper_type": "industry", @@ -23438,6 +23935,7 @@ "id": "IWSLT_1", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "other", @@ -23472,6 +23970,7 @@ "id": "IWSLT_10", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -23515,6 +24014,7 @@ "id": "IWSLT_11", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -23548,6 +24048,7 @@ "id": "IWSLT_12", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -23580,6 +24081,7 @@ "id": "IWSLT_13", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -23612,6 +24114,7 @@ "id": "IWSLT_14", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -23658,6 +24161,7 @@ "id": "IWSLT_16", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -23700,6 +24204,7 @@ "id": "IWSLT_17", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -23734,6 +24239,7 @@ "id": "IWSLT_2", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -23771,6 +24277,7 @@ "id": "IWSLT_21", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -23803,6 +24310,7 @@ "id": "IWSLT_22", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -23839,6 +24347,7 @@ "id": "IWSLT_23", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -23880,6 +24389,7 @@ "id": "IWSLT_24", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -23913,6 +24423,7 @@ "id": "IWSLT_25", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -23950,6 +24461,7 @@ "id": "IWSLT_26", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -23984,6 +24496,7 @@ "id": "IWSLT_28", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24019,6 +24532,7 @@ "id": "IWSLT_29", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24057,6 +24571,7 @@ "id": "IWSLT_3", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24089,6 +24604,7 @@ "id": "IWSLT_30", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24120,6 +24636,7 @@ "id": "IWSLT_31", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24158,6 +24675,7 @@ "id": "IWSLT_32", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24193,6 +24711,7 @@ "id": "IWSLT_33", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24225,6 +24744,7 @@ "id": "IWSLT_34", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24260,6 +24780,7 @@ "id": "IWSLT_35", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24298,6 +24819,7 @@ "id": "IWSLT_36", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24330,6 +24852,7 @@ "id": "IWSLT_37", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24369,6 +24892,7 @@ "id": "IWSLT_38", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24401,6 +24925,7 @@ "id": "IWSLT_39", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24433,6 +24958,7 @@ "id": "IWSLT_4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24467,6 +24993,7 @@ "id": "IWSLT_40", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24502,6 +25029,7 @@ "id": "IWSLT_41", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24544,6 +25072,7 @@ "id": "IWSLT_42", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24585,6 +25114,7 @@ "id": "IWSLT_43", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24620,6 +25150,7 @@ "id": "IWSLT_44", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24653,6 +25184,7 @@ "id": "IWSLT_45", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24692,6 +25224,7 @@ "id": "IWSLT_46", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24725,6 +25258,7 @@ "id": "IWSLT_47", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24764,6 +25298,7 @@ "id": "IWSLT_48", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24798,6 +25333,7 @@ "id": "IWSLT_5", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24831,6 +25367,7 @@ "id": "IWSLT_50", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24866,6 +25403,7 @@ "id": "IWSLT_51", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24898,6 +25436,7 @@ "id": "IWSLT_52", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24928,6 +25467,7 @@ "id": "IWSLT_53", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -24965,6 +25505,7 @@ "id": "IWSLT_54", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -25000,6 +25541,7 @@ "id": "IWSLT_55", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -25033,6 +25575,7 @@ "id": "IWSLT_56", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -25072,6 +25615,7 @@ "id": "IWSLT_7", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -25102,6 +25646,7 @@ "id": "IWSLT_8", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -25133,6 +25678,7 @@ "id": "LAW_11", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25165,6 +25711,7 @@ "id": "LAW_12", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25201,6 +25748,7 @@ "id": "LAW_13", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short paper (4 pages)", @@ -25233,6 +25781,7 @@ "id": "LAW_18", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25266,6 +25815,7 @@ "id": "LAW_20", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25299,6 +25849,7 @@ "id": "LAW_22", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25331,6 +25882,7 @@ "id": "LAW_26", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short paper (4 pages)", @@ -25364,6 +25916,7 @@ "id": "LAW_27", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25400,6 +25953,7 @@ "id": "LAW_28", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25431,6 +25985,7 @@ "id": "LAW_33", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25466,6 +26021,7 @@ "id": "LAW_34", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short paper (4 pages)", @@ -25498,6 +26054,7 @@ "id": "LAW_38", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short paper (4 pages)", @@ -25529,6 +26086,7 @@ "id": "LAW_40", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25567,6 +26125,7 @@ "id": "LAW_41", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25599,6 +26158,7 @@ "id": "LAW_42", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25634,6 +26194,7 @@ "id": "LAW_44", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25666,6 +26227,7 @@ "id": "LAW_47", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25701,6 +26263,7 @@ "id": "LAW_48", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short paper (4 pages)", @@ -25733,6 +26296,7 @@ "id": "LAW_50", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25766,6 +26330,7 @@ "id": "LAW_51", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25800,6 +26365,7 @@ "id": "LAW_52", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short paper (4 pages)", @@ -25831,6 +26397,7 @@ "id": "LAW_54", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25863,6 +26430,7 @@ "id": "LAW_58", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25895,6 +26463,7 @@ "id": "LAW_7", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25925,6 +26494,7 @@ "id": "LAW_8", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25955,6 +26525,7 @@ "id": "LAW_9", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long paper (8 pages)", @@ -25986,6 +26557,7 @@ "id": "LAW_F1", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -26021,6 +26593,7 @@ "id": "LAW_F2", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -26060,6 +26633,7 @@ "id": "LAW_F3", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -26094,6 +26668,7 @@ "id": "LAW_F4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -26135,6 +26710,7 @@ "id": "LAW_F5", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -26173,6 +26749,7 @@ "id": "LAW_F6", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -26206,6 +26783,7 @@ "id": "LAW_F7", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -26237,6 +26815,7 @@ "id": "LAW_F8", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -26272,6 +26851,7 @@ "id": "LAW_F9", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -26310,6 +26890,7 @@ "id": "MATCHING_F1", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26343,6 +26924,7 @@ "id": "MATCHING_F3", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26377,6 +26959,7 @@ "id": "MATCHING_F4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26411,6 +26994,7 @@ "id": "MATCHING_F5", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26443,6 +27027,7 @@ "id": "MATCHING_F6", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26475,6 +27060,7 @@ "id": "MATCHING_M1", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26505,6 +27091,7 @@ "id": "MATCHING_M11", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26537,6 +27124,7 @@ "id": "MATCHING_M12", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26569,6 +27157,7 @@ "id": "MATCHING_M14", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26601,6 +27190,7 @@ "id": "MATCHING_M15", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26636,6 +27226,7 @@ "id": "MATCHING_M16", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26667,6 +27258,7 @@ "id": "MATCHING_M6", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26700,6 +27292,7 @@ "id": "MATCHING_M8", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26734,6 +27327,7 @@ "id": "MATCHING_M9", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26767,6 +27361,7 @@ "id": "NLP4ConvAI_12", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26799,6 +27394,7 @@ "id": "NLP4ConvAI_14", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26832,6 +27428,7 @@ "id": "NLP4ConvAI_17", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -26863,6 +27460,7 @@ "id": "NLP4ConvAI_19", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26894,6 +27492,7 @@ "id": "NLP4ConvAI_21", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26927,6 +27526,7 @@ "id": "NLP4ConvAI_22", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26960,6 +27560,7 @@ "id": "NLP4ConvAI_23", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -26992,6 +27593,7 @@ "id": "NLP4ConvAI_35", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -27023,6 +27625,7 @@ "id": "NLP4ConvAI_37", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -27055,6 +27658,7 @@ "id": "NLP4ConvAI_40", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -27087,6 +27691,7 @@ "id": "NLP4ConvAI_50", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -27122,6 +27727,7 @@ "id": "NLP4ConvAI_53", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -27154,6 +27760,7 @@ "id": "NLP4ConvAI_7", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -27187,6 +27794,7 @@ "id": "NLP4ConvAI_F1", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -27218,6 +27826,7 @@ "id": "NLP4ConvAI_F2", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -27253,6 +27862,7 @@ "id": "NLP4ConvAI_F3", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -27286,6 +27896,7 @@ "id": "NLP4ConvAI_F4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -27322,6 +27933,7 @@ "id": "NLP4ConvAI_F5", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -27355,6 +27967,7 @@ "id": "NLP4ConvAI_F6", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -27386,6 +27999,7 @@ "id": "NLP4ConvAI_F7", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -27418,7 +28032,10 @@ ], "id": "P1012", "is_paper": true, - "keywords": [], + "keywords": [ + "security/privacy" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.117.pdf", "paper_type": "Main-Poster", @@ -27453,7 +28070,10 @@ ], "id": "P1015", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.128.pdf", "paper_type": "Main-Poster", @@ -27489,7 +28109,10 @@ ], "id": "P1018", "is_paper": true, - "keywords": [], + "keywords": [ + "inference methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.281.pdf", "paper_type": "findings", @@ -27522,7 +28145,10 @@ ], "id": "P1022", "is_paper": true, - "keywords": [], + "keywords": [ + "open-domain qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.807.pdf", "paper_type": "Main-Oral", @@ -27559,7 +28185,12 @@ ], "id": "P1027", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.826.pdf", "paper_type": "Main-Poster", @@ -27593,7 +28224,11 @@ ], "id": "P1033", "is_paper": true, - "keywords": [], + "keywords": [ + "applications", + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.82.pdf", "paper_type": "Main-Poster", @@ -27630,7 +28265,10 @@ ], "id": "P1040", "is_paper": true, - "keywords": [], + "keywords": [ + "lessons from deployment" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.740.pdf", "paper_type": "findings", @@ -27665,7 +28303,10 @@ ], "id": "P1047", "is_paper": true, - "keywords": [], + "keywords": [ + "misinformation detection and analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.756.pdf", "paper_type": "findings", @@ -27698,7 +28339,10 @@ ], "id": "P1056", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.862.pdf", "paper_type": "findings", @@ -27733,7 +28377,10 @@ ], "id": "P1059", "is_paper": true, - "keywords": [], + "keywords": [ + "online adaptation for mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.332.pdf", "paper_type": "Main-Poster", @@ -27768,7 +28415,10 @@ ], "id": "P106", "is_paper": true, - "keywords": [], + "keywords": [ + "style analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.772.pdf", "paper_type": "findings", @@ -27801,7 +28451,10 @@ ], "id": "P1065", "is_paper": true, - "keywords": [], + "keywords": [ + "generalization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.895.pdf", "paper_type": "findings", @@ -27844,7 +28497,10 @@ ], "id": "P1067", "is_paper": true, - "keywords": [], + "keywords": [ + "vision question answering" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.147.pdf", "paper_type": "findings", @@ -27880,7 +28536,10 @@ ], "id": "P1069", "is_paper": true, - "keywords": [], + "keywords": [ + "table qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.278.pdf", "paper_type": "Main-Poster", @@ -27913,7 +28572,10 @@ ], "id": "P1071", "is_paper": true, - "keywords": [], + "keywords": [ + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.164.pdf", "paper_type": "Main-Poster", @@ -27945,7 +28607,11 @@ ], "id": "P1072", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.830.pdf", "paper_type": "Main-Poster", @@ -27979,7 +28645,10 @@ ], "id": "P1074", "is_paper": true, - "keywords": [], + "keywords": [ + "graph-based methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.432.pdf", "paper_type": "Main-Poster", @@ -28010,7 +28679,10 @@ ], "id": "P1075", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.47.pdf", "paper_type": "findings", @@ -28049,7 +28721,10 @@ ], "id": "P1078", "is_paper": true, - "keywords": [], + "keywords": [ + "mutlilingual representations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.191.pdf", "paper_type": "Main-Poster", @@ -28082,7 +28757,10 @@ ], "id": "P1080", "is_paper": true, - "keywords": [], + "keywords": [ + "stance detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.220.pdf", "paper_type": "Main-Oral", @@ -28121,7 +28799,10 @@ ], "id": "P1084", "is_paper": true, - "keywords": [], + "keywords": [ + "continual learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.723.pdf", "paper_type": "findings", @@ -28165,7 +28846,12 @@ ], "id": "P1087", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.499.pdf", "paper_type": "Main-Poster", @@ -28201,7 +28887,14 @@ ], "id": "P1090", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge augmented", + "applications", + "conversational modeling" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.299.pdf", "paper_type": "Main-Poster", @@ -28235,7 +28928,10 @@ ], "id": "P1099", "is_paper": true, - "keywords": [], + "keywords": [ + "financial/business nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.412.pdf", "paper_type": "Main-Poster", @@ -28268,7 +28964,11 @@ ], "id": "P110", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation", + "model architectures" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.558.pdf", "paper_type": "findings", @@ -28299,7 +28999,10 @@ ], "id": "P1101", "is_paper": true, - "keywords": [], + "keywords": [ + "semantic textual similarity" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.49.pdf", "paper_type": "Main-Poster", @@ -28337,7 +29040,11 @@ ], "id": "P1102", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge augmented", + "grounded dialog" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.149.pdf", "paper_type": "Main-Poster", @@ -28370,7 +29077,10 @@ ], "id": "P1126", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.730.pdf", "paper_type": "Main-Oral", @@ -28401,7 +29111,10 @@ ], "id": "P1132", "is_paper": true, - "keywords": [], + "keywords": [ + "argument quality assessment" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.880.pdf", "paper_type": "Main-Poster", @@ -28436,7 +29149,10 @@ ], "id": "P1133", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational summarization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.76.pdf", "paper_type": "Main-Poster", @@ -28472,7 +29188,11 @@ ], "id": "P1139", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal content generation", + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.442.pdf", "paper_type": "Main-Poster", @@ -28505,7 +29225,11 @@ ], "id": "P1142", "is_paper": true, - "keywords": [], + "keywords": [ + "polysemy", + "word embeddings" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.808.pdf", "paper_type": "findings", @@ -28537,7 +29261,10 @@ ], "id": "P1143", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.39.pdf", "paper_type": "Main-Poster", @@ -28571,7 +29298,10 @@ ], "id": "P1148", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.651.pdf", "paper_type": "Main-Oral", @@ -28607,7 +29337,10 @@ ], "id": "P1149", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.704.pdf", "paper_type": "Main-Poster", @@ -28642,7 +29375,10 @@ ], "id": "P115", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.16.pdf", "paper_type": "Main-Poster", @@ -28675,7 +29411,13 @@ ], "id": "P1150", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation", + "mt deployment and maintainence" + ], + "languages": [ + "german" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.770.pdf", "paper_type": "Main-Poster", @@ -28708,7 +29450,10 @@ ], "id": "P1154", "is_paper": true, - "keywords": [], + "keywords": [ + "code generation and understanding" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.823.pdf", "paper_type": "findings", @@ -28742,7 +29487,10 @@ ], "id": "P116", "is_paper": true, - "keywords": [], + "keywords": [ + "natural language inference" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.403.pdf", "paper_type": "Main-Poster", @@ -28775,7 +29523,11 @@ ], "id": "P1160", "is_paper": true, - "keywords": [], + "keywords": [ + "dense retrieval", + "re-ranking" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.558.pdf", "paper_type": "Main-Poster", @@ -28807,7 +29559,10 @@ ], "id": "P1162", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.482.pdf", "paper_type": "Main-Poster", @@ -28839,7 +29594,10 @@ ], "id": "P1166", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge-augmented methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.207.pdf", "paper_type": "Main-Poster", @@ -28872,7 +29630,10 @@ ], "id": "P1168", "is_paper": true, - "keywords": [], + "keywords": [ + "phrase/sentence embedding" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.778.pdf", "paper_type": "findings", @@ -28903,7 +29664,16 @@ ], "id": "P1170", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "benchmarking", + "automatic creation and evaluation of language resources", + "datasets for low resource languages" + ], + "languages": [ + "ancient greek", + "latin" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.846.pdf", "paper_type": "Main-Poster", @@ -28938,7 +29708,10 @@ ], "id": "P1173", "is_paper": true, - "keywords": [], + "keywords": [ + "parameter-efficient finetuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.840.pdf", "paper_type": "Main-Poster", @@ -28973,7 +29746,10 @@ ], "id": "P1178", "is_paper": true, - "keywords": [], + "keywords": [ + "document-level extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.62.pdf", "paper_type": "Main-Poster", @@ -29007,7 +29783,10 @@ ], "id": "P118", "is_paper": true, - "keywords": [], + "keywords": [ + "efficient inference for mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.812.pdf", "paper_type": "Main-Poster", @@ -29038,7 +29817,12 @@ ], "id": "P1180", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [ + "german" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.103.pdf", "paper_type": "Main-Poster", @@ -29076,7 +29860,10 @@ ], "id": "P1183", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.195.pdf", "paper_type": "Main-Poster", @@ -29111,7 +29898,10 @@ ], "id": "P1185", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.297.pdf", "paper_type": "Main-Oral", @@ -29145,7 +29935,10 @@ ], "id": "P1190", "is_paper": true, - "keywords": [], + "keywords": [ + "argument mining" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.303.pdf", "paper_type": "findings", @@ -29181,7 +29974,10 @@ ], "id": "P1191", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational summarization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.775.pdf", "paper_type": "Main-Poster", @@ -29218,7 +30014,10 @@ ], "id": "P1195", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial attacks/examples/training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.680.pdf", "paper_type": "Main-Poster", @@ -29252,7 +30051,14 @@ ], "id": "P1200", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot/zero-shot mt", + "multilingual mt", + "multimodality" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.329.pdf", "paper_type": "Main-Poster", @@ -29291,7 +30097,15 @@ ], "id": "P1205", "is_paper": true, - "keywords": [], + "keywords": [ + "benchmarking", + "language resources", + "multilingual corpora", + "nlp datasets" + ], + "languages": [ + "bulgarian" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.487.pdf", "paper_type": "Main-Poster", @@ -29324,7 +30138,12 @@ ], "id": "P1206", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented", + "factuality", + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.595.pdf", "paper_type": "Main-Poster", @@ -29360,7 +30179,11 @@ ], "id": "P1207", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.153.pdf", "paper_type": "Main-Poster", @@ -29399,7 +30222,13 @@ ], "id": "P1212", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge augmented", + "grounded dialog" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.224.pdf", "paper_type": "findings", @@ -29431,7 +30260,11 @@ ], "id": "P1213", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented", + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.209.pdf", "paper_type": "Main-Poster", @@ -29463,7 +30296,12 @@ ], "id": "P1223", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented", + "factuality", + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.140.pdf", "paper_type": "findings", @@ -29497,7 +30335,10 @@ ], "id": "P1227", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.86.pdf", "paper_type": "Main-Poster", @@ -29529,7 +30370,10 @@ ], "id": "P1228", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.834.pdf", "paper_type": "findings", @@ -29565,7 +30409,12 @@ ], "id": "P1236", "is_paper": true, - "keywords": [], + "keywords": [ + "(non-)generalizability", + "evaluation", + "methodology" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.319.pdf", "paper_type": "Main-Poster", @@ -29599,7 +30448,11 @@ ], "id": "P1239", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial attacks/examples/training", + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.282.pdf", "paper_type": "Main-Poster", @@ -29634,7 +30487,10 @@ ], "id": "P1242", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.841.pdf", "paper_type": "Main-Poster", @@ -29666,7 +30522,10 @@ ], "id": "P1243", "is_paper": true, - "keywords": [], + "keywords": [ + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.658.pdf", "paper_type": "Main-Poster", @@ -29700,7 +30559,11 @@ ], "id": "P1245", "is_paper": true, - "keywords": [], + "keywords": [ + "data influence", + "feature attribution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.669.pdf", "paper_type": "Main-Oral", @@ -29734,7 +30597,14 @@ ], "id": "P1254", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal matchine translation" + ], + "languages": [ + "french", + "german", + "czech" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.295.pdf", "paper_type": "Main-Poster", @@ -29767,7 +30637,11 @@ ], "id": "P1258", "is_paper": true, - "keywords": [], + "keywords": [ + "representation learning", + "model compression methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.625.pdf", "paper_type": "findings", @@ -29799,7 +30673,10 @@ ], "id": "P1265", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.644.pdf", "paper_type": "Main-Oral", @@ -29831,7 +30708,10 @@ ], "id": "P1266", "is_paper": true, - "keywords": [], + "keywords": [ + "generative models" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.592.pdf", "paper_type": "Main-Poster", @@ -29870,7 +30750,12 @@ ], "id": "P1270", "is_paper": true, - "keywords": [], + "keywords": [ + "speech and vision", + "speech technologies", + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.424.pdf", "paper_type": "findings", @@ -29902,7 +30787,10 @@ ], "id": "P1275", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic speech recognition" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.67.pdf", "paper_type": "Main-Poster", @@ -29939,7 +30827,10 @@ ], "id": "P1276", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.698.pdf", "paper_type": "Main-Poster", @@ -29972,7 +30863,10 @@ ], "id": "P1277", "is_paper": true, - "keywords": [], + "keywords": [ + "self-supervised learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.830.pdf", "paper_type": "findings", @@ -30006,7 +30900,10 @@ ], "id": "P1278", "is_paper": true, - "keywords": [], + "keywords": [ + "scaling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.19.pdf", "paper_type": "findings", @@ -30041,7 +30938,10 @@ ], "id": "P1284", "is_paper": true, - "keywords": [], + "keywords": [ + "feature attribution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.572.pdf", "paper_type": "Main-Poster", @@ -30074,7 +30974,11 @@ ], "id": "P1286", "is_paper": true, - "keywords": [], + "keywords": [ + "bridging resolution", + "dialogue" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.688.pdf", "paper_type": "Main-Poster", @@ -30109,7 +31013,10 @@ ], "id": "P129", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-task learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.144.pdf", "paper_type": "Main-Poster", @@ -30143,7 +31050,10 @@ ], "id": "P1291", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.202.pdf", "paper_type": "findings", @@ -30177,7 +31087,10 @@ ], "id": "P1292", "is_paper": true, - "keywords": [], + "keywords": [ + "code generation and understanding" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.45.pdf", "paper_type": "Main-Poster", @@ -30209,7 +31122,10 @@ ], "id": "P1297", "is_paper": true, - "keywords": [], + "keywords": [ + "transfer learning / domain adaptation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.524.pdf", "paper_type": "findings", @@ -30244,7 +31160,12 @@ ], "id": "P1299", "is_paper": true, - "keywords": [], + "keywords": [ + "passage retrieval", + "dense retrieval", + "contrastive learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.140.pdf", "paper_type": "Main-Oral", @@ -30277,7 +31198,10 @@ ], "id": "P1302", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.1.pdf", "paper_type": "Main-Poster", @@ -30326,7 +31250,20 @@ ], "id": "P1303", "is_paper": true, - "keywords": [], + "keywords": [ + "code-switching", + "mixed language", + "multilingualism", + "cross-lingual transfer", + "multilingual pre-training", + "multilingual benchmarks", + "multilingual evaluation" + ], + "languages": [ + "chinese", + "french", + "hindi" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.174.pdf", "paper_type": "findings", @@ -30359,6 +31296,7 @@ "id": "P1306", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.648.pdf", "paper_type": "findings", @@ -30393,7 +31331,10 @@ ], "id": "P1309", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.215.pdf", "paper_type": "Main-Poster", @@ -30428,7 +31369,10 @@ ], "id": "P1312", "is_paper": true, - "keywords": [], + "keywords": [ + "model architectures" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.805.pdf", "paper_type": "findings", @@ -30461,7 +31405,11 @@ ], "id": "P1315", "is_paper": true, - "keywords": [], + "keywords": [ + "code-switching", + "multilingual pre-training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.66.pdf", "paper_type": "Main-Poster", @@ -30494,7 +31442,10 @@ ], "id": "P1316", "is_paper": true, - "keywords": [], + "keywords": [ + "self-supervised learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.48.pdf", "paper_type": "Main-Poster", @@ -30528,7 +31479,10 @@ ], "id": "P1322", "is_paper": true, - "keywords": [], + "keywords": [ + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.516.pdf", "paper_type": "Main-Poster", @@ -30561,7 +31515,11 @@ ], "id": "P1323", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-modal dialogue systems", + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.791.pdf", "paper_type": "Main-Poster", @@ -30599,7 +31557,12 @@ ], "id": "P1325", "is_paper": true, - "keywords": [], + "keywords": [ + "generalization", + "few-shot qa", + "table qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.341.pdf", "paper_type": "findings", @@ -30632,7 +31595,10 @@ ], "id": "P1328", "is_paper": true, - "keywords": [], + "keywords": [ + "(non-)generalizability" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.144.pdf", "paper_type": "Main-Poster", @@ -30666,7 +31632,10 @@ ], "id": "P1329", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.102.pdf", "paper_type": "Main-Poster", @@ -30703,7 +31672,12 @@ ], "id": "P133", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented", + "multilingual / low resource", + "dialogue state tracking" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.114.pdf", "paper_type": "Main-Oral", @@ -30737,7 +31711,10 @@ ], "id": "P1333", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/unfairness mitigation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.336.pdf", "paper_type": "findings", @@ -30772,7 +31749,12 @@ ], "id": "P1340", "is_paper": true, - "keywords": [], + "keywords": [ + "bias/toxicity" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.757.pdf", "paper_type": "Main-Poster", @@ -30810,7 +31792,10 @@ ], "id": "P1342", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.797.pdf", "paper_type": "findings", @@ -30849,7 +31834,10 @@ ], "id": "P1345", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal content generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.804.pdf", "paper_type": "Main-Poster", @@ -30886,7 +31874,10 @@ ], "id": "P1348", "is_paper": true, - "keywords": [], + "keywords": [ + "vocabulary learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.38.pdf", "paper_type": "findings", @@ -30921,7 +31912,10 @@ ], "id": "P1349", "is_paper": true, - "keywords": [], + "keywords": [ + "vocabulary learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.284.pdf", "paper_type": "Main-Poster", @@ -30956,7 +31950,11 @@ ], "id": "P1355", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot generation", + "interactive and collaborative generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.758.pdf", "paper_type": "findings", @@ -30991,7 +31989,10 @@ ], "id": "P1362", "is_paper": true, - "keywords": [], + "keywords": [ + "open-domain qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.354.pdf", "paper_type": "findings", @@ -31024,7 +32025,15 @@ ], "id": "P1365", "is_paper": true, - "keywords": [], + "keywords": [ + "less-resourced languages" + ], + "languages": [ + "tigrinya", + "east african", + "afro-asiatic", + "semitic" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.661.pdf", "paper_type": "Main-Oral", @@ -31068,7 +32077,10 @@ ], "id": "P1368", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.653.pdf", "paper_type": "Main-Poster", @@ -31099,7 +32111,12 @@ ], "id": "P1370", "is_paper": true, - "keywords": [], + "keywords": [ + "self-supervised learning", + "representation learning", + "few-shot learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.339.pdf", "paper_type": "Main-Poster", @@ -31134,7 +32151,12 @@ ], "id": "P1378", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation" + ], + "languages": [ + "korean" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.414.pdf", "paper_type": "findings", @@ -31170,7 +32192,10 @@ ], "id": "P138", "is_paper": true, - "keywords": [], + "keywords": [ + "representation learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.104.pdf", "paper_type": "Main-Poster", @@ -31201,7 +32226,12 @@ ], "id": "P1381", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation of datasets", + "evaluation methodologies", + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.870.pdf", "paper_type": "Main-Poster", @@ -31235,7 +32265,10 @@ ], "id": "P1388", "is_paper": true, - "keywords": [], + "keywords": [ + "financial/business nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.800.pdf", "paper_type": "Main-Poster", @@ -31272,7 +32305,10 @@ ], "id": "P1391", "is_paper": true, - "keywords": [], + "keywords": [ + "methodology" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.382.pdf", "paper_type": "findings", @@ -31309,7 +32345,11 @@ ], "id": "P1398", "is_paper": true, - "keywords": [], + "keywords": [ + "commonsense qa", + "multihop qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.6.pdf", "paper_type": "findings", @@ -31346,7 +32386,10 @@ ], "id": "P140", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.411.pdf", "paper_type": "Main-Poster", @@ -31382,7 +32425,10 @@ ], "id": "P1402", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.322.pdf", "paper_type": "findings", @@ -31415,7 +32461,10 @@ ], "id": "P1411", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.285.pdf", "paper_type": "findings", @@ -31449,7 +32498,10 @@ ], "id": "P1424", "is_paper": true, - "keywords": [], + "keywords": [ + "reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.671.pdf", "paper_type": "Main-Poster", @@ -31485,7 +32537,11 @@ ], "id": "P1430", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.618.pdf", "paper_type": "Main-Poster", @@ -31520,7 +32576,10 @@ ], "id": "P1433", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.121.pdf", "paper_type": "Main-Oral", @@ -31552,7 +32611,10 @@ ], "id": "P1436", "is_paper": true, - "keywords": [], + "keywords": [ + "polysemy" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.245.pdf", "paper_type": "findings", @@ -31586,7 +32648,10 @@ ], "id": "P1448", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.129.pdf", "paper_type": "Main-Poster", @@ -31618,7 +32683,10 @@ ], "id": "P1449", "is_paper": true, - "keywords": [], + "keywords": [ + "mt theory" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.599.pdf", "paper_type": "Main-Poster", @@ -31651,7 +32719,10 @@ ], "id": "P1452", "is_paper": true, - "keywords": [], + "keywords": [ + "continual learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.109.pdf", "paper_type": "Main-Poster", @@ -31687,7 +32758,10 @@ ], "id": "P1453", "is_paper": true, - "keywords": [], + "keywords": [ + "style analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.515.pdf", "paper_type": "Main-Poster", @@ -31719,7 +32793,11 @@ ], "id": "P1455", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge base qa", + "semantic parsing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.270.pdf", "paper_type": "Main-Poster", @@ -31750,7 +32828,10 @@ ], "id": "P1459", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge-augmented methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.208.pdf", "paper_type": "Main-Poster", @@ -31786,7 +32867,10 @@ ], "id": "P1463", "is_paper": true, - "keywords": [], + "keywords": [ + "switch-code translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.673.pdf", "paper_type": "findings", @@ -31821,7 +32905,10 @@ ], "id": "P1464", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.504.pdf", "paper_type": "findings", @@ -31859,7 +32946,10 @@ ], "id": "P1465", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.411.pdf", "paper_type": "findings", @@ -31891,7 +32981,11 @@ ], "id": "P1466", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge augmented", + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.433.pdf", "paper_type": "Main-Poster", @@ -31924,7 +33018,11 @@ ], "id": "P1471", "is_paper": true, - "keywords": [], + "keywords": [ + "style generation", + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.827.pdf", "paper_type": "Main-Poster", @@ -31958,7 +33056,11 @@ ], "id": "P1475", "is_paper": true, - "keywords": [], + "keywords": [ + "argument mining", + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.544.pdf", "paper_type": "Main-Poster", @@ -31992,7 +33094,10 @@ ], "id": "P1476", "is_paper": true, - "keywords": [], + "keywords": [ + "document-level extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.739.pdf", "paper_type": "Main-Poster", @@ -32030,7 +33135,10 @@ ], "id": "P1477", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodal qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.714.pdf", "paper_type": "Main-Poster", @@ -32072,7 +33180,10 @@ ], "id": "P1479", "is_paper": true, - "keywords": [], + "keywords": [ + "mathematical nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.706.pdf", "paper_type": "Main-Poster", @@ -32108,7 +33219,10 @@ ], "id": "P1482", "is_paper": true, - "keywords": [], + "keywords": [ + "data-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.836.pdf", "paper_type": "Main-Poster", @@ -32142,7 +33256,10 @@ ], "id": "P1495", "is_paper": true, - "keywords": [], + "keywords": [ + "efficient models" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.96.pdf", "paper_type": "Main-Poster", @@ -32175,7 +33292,12 @@ ], "id": "P1498", "is_paper": true, - "keywords": [], + "keywords": [ + "dependency parsing" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.607.pdf", "paper_type": "findings", @@ -32211,7 +33333,10 @@ ], "id": "P1500", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.820.pdf", "paper_type": "findings", @@ -32244,7 +33369,12 @@ ], "id": "P1504", "is_paper": true, - "keywords": [], + "keywords": [ + "human-in-the-loop", + "grounded dialog", + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.96.pdf", "paper_type": "Main-Poster", @@ -32278,7 +33408,10 @@ ], "id": "P1509", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial attacks/examples/training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.40.pdf", "paper_type": "Main-Poster", @@ -32310,7 +33443,10 @@ ], "id": "P1513", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.464.pdf", "paper_type": "findings", @@ -32342,7 +33478,12 @@ ], "id": "P1516", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation", + "conversational summarization", + "factuality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.377.pdf", "paper_type": "Main-Poster", @@ -32377,7 +33518,10 @@ ], "id": "P1518", "is_paper": true, - "keywords": [], + "keywords": [ + "paraphrase generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.447.pdf", "paper_type": "Main-Poster", @@ -32413,6 +33557,7 @@ "id": "P1520", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.366.pdf", "paper_type": "findings", @@ -32445,7 +33590,12 @@ ], "id": "P1530", "is_paper": true, - "keywords": [], + "keywords": [ + "benchmarking", + "nlp datasets", + "metrics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.452.pdf", "paper_type": "findings", @@ -32478,7 +33628,11 @@ ], "id": "P1538", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot generation", + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.667.pdf", "paper_type": "findings", @@ -32514,7 +33668,10 @@ ], "id": "P1541", "is_paper": true, - "keywords": [], + "keywords": [ + "open information extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.907.pdf", "paper_type": "Main-Poster", @@ -32548,7 +33705,10 @@ ], "id": "P1542", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.222.pdf", "paper_type": "Main-Poster", @@ -32582,7 +33742,10 @@ ], "id": "P1545", "is_paper": true, - "keywords": [], + "keywords": [ + "causality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.720.pdf", "paper_type": "findings", @@ -32618,7 +33781,10 @@ ], "id": "P1550", "is_paper": true, - "keywords": [], + "keywords": [ + "zero/few-shot extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.339.pdf", "paper_type": "findings", @@ -32651,7 +33817,10 @@ ], "id": "P1555", "is_paper": true, - "keywords": [], + "keywords": [ + "psycho-demographic trait prediction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.681.pdf", "paper_type": "Main-Poster", @@ -32686,7 +33855,12 @@ ], "id": "P1569", "is_paper": true, - "keywords": [], + "keywords": [ + "language resources" + ], + "languages": [ + "simplified chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.898.pdf", "paper_type": "Main-Poster", @@ -32721,7 +33895,10 @@ ], "id": "P157", "is_paper": true, - "keywords": [], + "keywords": [ + "modelling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.430.pdf", "paper_type": "findings", @@ -32759,7 +33936,10 @@ ], "id": "P1572", "is_paper": true, - "keywords": [], + "keywords": [ + "domain adaptation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.441.pdf", "paper_type": "Main-Poster", @@ -32798,7 +33978,10 @@ ], "id": "P1575", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal content generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.694.pdf", "paper_type": "Main-Poster", @@ -32833,7 +34016,10 @@ ], "id": "P1580", "is_paper": true, - "keywords": [], + "keywords": [ + "passage retrieval" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.336.pdf", "paper_type": "Main-Poster", @@ -32864,7 +34050,10 @@ ], "id": "P1583", "is_paper": true, - "keywords": [], + "keywords": [ + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.40.pdf", "paper_type": "findings", @@ -32899,7 +34088,10 @@ ], "id": "P1588", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.248.pdf", "paper_type": "Main-Poster", @@ -32935,7 +34127,13 @@ ], "id": "P1593", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal content generation", + "cross-modal application" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.37.pdf", "paper_type": "findings", @@ -32974,7 +34172,10 @@ ], "id": "P1595", "is_paper": true, - "keywords": [], + "keywords": [ + "open information extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.272.pdf", "paper_type": "Main-Poster", @@ -33009,7 +34210,10 @@ ], "id": "P1605", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-modal dialogue systems" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.749.pdf", "paper_type": "Main-Poster", @@ -33041,7 +34245,19 @@ ], "id": "P1621", "is_paper": true, - "keywords": [], + "keywords": [ + "morphological segmentation", + "subword representations", + "morphological analysis" + ], + "languages": [ + "xhosa", + "zulu", + "finnish", + "swati", + "tswana", + "afrikaans" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.175.pdf", "paper_type": "findings", @@ -33077,7 +34293,10 @@ ], "id": "P1623", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge graphs" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.488.pdf", "paper_type": "findings", @@ -33111,7 +34330,10 @@ ], "id": "P1624", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.229.pdf", "paper_type": "Main-Poster", @@ -33146,7 +34368,12 @@ ], "id": "P1626", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "benchmarking", + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.2.pdf", "paper_type": "Main-Oral", @@ -33179,7 +34406,10 @@ ], "id": "P1627", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.79.pdf", "paper_type": "Main-Oral", @@ -33212,7 +34442,10 @@ ], "id": "P1634", "is_paper": true, - "keywords": [], + "keywords": [ + "question generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.500.pdf", "paper_type": "Main-Poster", @@ -33246,7 +34479,10 @@ ], "id": "P1636", "is_paper": true, - "keywords": [], + "keywords": [ + "free-text/natural language explanations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.765.pdf", "paper_type": "findings", @@ -33279,7 +34515,10 @@ ], "id": "P1637", "is_paper": true, - "keywords": [], + "keywords": [ + "psycho-demographic trait prediction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.198.pdf", "paper_type": "findings", @@ -33315,7 +34554,10 @@ ], "id": "P1638", "is_paper": true, - "keywords": [], + "keywords": [ + "stance detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.19.pdf", "paper_type": "Main-Poster", @@ -33347,7 +34589,11 @@ ], "id": "P1645", "is_paper": true, - "keywords": [], + "keywords": [ + "structured prediction", + "graphical models" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.58.pdf", "paper_type": "findings", @@ -33378,7 +34624,10 @@ ], "id": "P1653", "is_paper": true, - "keywords": [], + "keywords": [ + "stance detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.127.pdf", "paper_type": "Main-Poster", @@ -33410,7 +34659,10 @@ ], "id": "P1658", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.65.pdf", "paper_type": "Main-Oral", @@ -33441,7 +34693,12 @@ ], "id": "P1662", "is_paper": true, - "keywords": [], + "keywords": [ + "lexical relationships", + "compositionality", + "interpretability" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.2.pdf", "paper_type": "findings", @@ -33479,7 +34736,12 @@ ], "id": "P1667", "is_paper": true, - "keywords": [], + "keywords": [ + "spoken dialogue systems", + "task-oriented", + "multi-modal dialogue systems" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.438.pdf", "paper_type": "Main-Poster", @@ -33513,7 +34775,10 @@ ], "id": "P1670", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.843.pdf", "paper_type": "findings", @@ -33546,7 +34811,10 @@ ], "id": "P1674", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/unfairness mitigation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.797.pdf", "paper_type": "Main-Poster", @@ -33583,7 +34851,10 @@ ], "id": "P1676", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.731.pdf", "paper_type": "findings", @@ -33619,7 +34890,10 @@ ], "id": "P1678", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.110.pdf", "paper_type": "findings", @@ -33652,7 +34926,10 @@ ], "id": "P1679", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.832.pdf", "paper_type": "Main-Poster", @@ -33685,7 +34962,10 @@ ], "id": "P1680", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.825.pdf", "paper_type": "Main-Poster", @@ -33717,7 +34997,11 @@ ], "id": "P1684", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented", + "dialogue state tracking" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.252.pdf", "paper_type": "Main-Poster", @@ -33752,7 +35036,10 @@ ], "id": "P1691", "is_paper": true, - "keywords": [], + "keywords": [ + "argument mining" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.777.pdf", "paper_type": "findings", @@ -33790,7 +35077,10 @@ ], "id": "P1694", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.735.pdf", "paper_type": "findings", @@ -33827,7 +35117,10 @@ ], "id": "P1695", "is_paper": true, - "keywords": [], + "keywords": [ + "reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.347.pdf", "paper_type": "Main-Poster", @@ -33860,7 +35153,10 @@ ], "id": "P1696", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual corpora" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.164.pdf", "paper_type": "Main-Poster", @@ -33893,7 +35189,12 @@ ], "id": "P1697", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.600.pdf", "paper_type": "Main-Poster", @@ -33925,7 +35226,11 @@ ], "id": "P1706", "is_paper": true, - "keywords": [], + "keywords": [ + "efficient inference for mt", + "multilingual mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.198.pdf", "paper_type": "Main-Poster", @@ -33957,7 +35262,10 @@ ], "id": "P1714", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.400.pdf", "paper_type": "Main-Poster", @@ -33996,7 +35304,10 @@ ], "id": "P1715", "is_paper": true, - "keywords": [], + "keywords": [ + "probing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.250.pdf", "paper_type": "findings", @@ -34032,7 +35343,10 @@ ], "id": "P1716", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge tracing/discovering/inducing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.860.pdf", "paper_type": "Main-Poster", @@ -34066,7 +35380,10 @@ ], "id": "P1719", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.221.pdf", "paper_type": "Main-Poster", @@ -34101,7 +35418,10 @@ ], "id": "P1721", "is_paper": true, - "keywords": [], + "keywords": [ + "document representation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.782.pdf", "paper_type": "Main-Poster", @@ -34133,7 +35453,14 @@ ], "id": "P1728", "is_paper": true, - "keywords": [], + "keywords": [ + "extractive summarisation", + "abstractive summarisation", + "multi-document summarization", + "architectures", + "factuality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.473.pdf", "paper_type": "Main-Oral", @@ -34169,7 +35496,11 @@ ], "id": "P1737", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual evaluation", + "dialects and language varieties" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.726.pdf", "paper_type": "Main-Poster", @@ -34203,7 +35534,10 @@ ], "id": "P1744", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.50.pdf", "paper_type": "Main-Poster", @@ -34241,7 +35575,10 @@ ], "id": "P1752", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.105.pdf", "paper_type": "Main-Poster", @@ -34275,7 +35612,10 @@ ], "id": "P176", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.703.pdf", "paper_type": "findings", @@ -34308,7 +35648,10 @@ ], "id": "P1772", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.529.pdf", "paper_type": "findings", @@ -34347,7 +35690,10 @@ ], "id": "P1778", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual pre-training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.522.pdf", "paper_type": "Main-Poster", @@ -34382,7 +35728,10 @@ ], "id": "P178", "is_paper": true, - "keywords": [], + "keywords": [ + "feature attribution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.483.pdf", "paper_type": "Main-Oral", @@ -34417,7 +35766,10 @@ ], "id": "P1780", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.655.pdf", "paper_type": "findings", @@ -34452,7 +35804,10 @@ ], "id": "P1785", "is_paper": true, - "keywords": [], + "keywords": [ + "online adaptation for mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.131.pdf", "paper_type": "Main-Poster", @@ -34492,7 +35847,11 @@ ], "id": "P1786", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial attacks/examples/training", + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.146.pdf", "paper_type": "findings", @@ -34526,7 +35885,10 @@ ], "id": "P1793", "is_paper": true, - "keywords": [], + "keywords": [ + "emotion detection and analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.606.pdf", "paper_type": "Main-Poster", @@ -34565,7 +35927,10 @@ ], "id": "P1798", "is_paper": true, - "keywords": [], + "keywords": [ + "security/privacy" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.717.pdf", "paper_type": "findings", @@ -34596,7 +35961,10 @@ ], "id": "P1801", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.751.pdf", "paper_type": "findings", @@ -34633,7 +36001,10 @@ ], "id": "P1806", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.192.pdf", "paper_type": "Main-Poster", @@ -34664,7 +36035,12 @@ ], "id": "P1808", "is_paper": true, - "keywords": [], + "keywords": [ + "benchmarking", + "nlp datasets", + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.838.pdf", "paper_type": "Main-Poster", @@ -34698,7 +36074,11 @@ ], "id": "P181", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-task learning", + "transfer learning / domain adaptation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.46.pdf", "paper_type": "Main-Poster", @@ -34731,7 +36111,11 @@ ], "id": "P1810", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation", + "model architectures" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.118.pdf", "paper_type": "findings", @@ -34765,7 +36149,10 @@ ], "id": "P1816", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.92.pdf", "paper_type": "findings", @@ -34797,7 +36184,11 @@ ], "id": "P1817", "is_paper": true, - "keywords": [], + "keywords": [ + "transfer learning / domain adaptation", + "parameter-efficient finetuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.233.pdf", "paper_type": "Main-Poster", @@ -34830,7 +36221,10 @@ ], "id": "P1820", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-document summarization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.13.pdf", "paper_type": "Main-Poster", @@ -34869,7 +36263,10 @@ ], "id": "P1821", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.352.pdf", "paper_type": "Main-Poster", @@ -34900,7 +36297,10 @@ ], "id": "P1823", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.340.pdf", "paper_type": "findings", @@ -34935,7 +36335,10 @@ ], "id": "P1829", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.866.pdf", "paper_type": "Main-Poster", @@ -34967,7 +36370,10 @@ ], "id": "P1831", "is_paper": true, - "keywords": [], + "keywords": [ + "word/phrase alignment" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.219.pdf", "paper_type": "Main-Poster", @@ -35000,7 +36406,10 @@ ], "id": "P1833", "is_paper": true, - "keywords": [], + "keywords": [ + "model compression methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.156.pdf", "paper_type": "Main-Poster", @@ -35041,7 +36450,12 @@ ], "id": "P1834", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training" + ], + "languages": [ + "arabic" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.181.pdf", "paper_type": "findings", @@ -35076,7 +36490,10 @@ ], "id": "P1843", "is_paper": true, - "keywords": [], + "keywords": [ + "model compression methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.803.pdf", "paper_type": "Main-Poster", @@ -35110,7 +36527,10 @@ ], "id": "P1847", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.374.pdf", "paper_type": "findings", @@ -35141,7 +36561,10 @@ ], "id": "P1854", "is_paper": true, - "keywords": [], + "keywords": [ + "constituency parsing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.469.pdf", "paper_type": "Main-Oral", @@ -35176,7 +36599,10 @@ ], "id": "P1855", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.330.pdf", "paper_type": "Main-Poster", @@ -35211,7 +36637,10 @@ ], "id": "P1856", "is_paper": true, - "keywords": [], + "keywords": [ + "optimization methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.243.pdf", "paper_type": "Main-Poster", @@ -35245,7 +36674,10 @@ ], "id": "P1857", "is_paper": true, - "keywords": [], + "keywords": [ + "re-ranking" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.61.pdf", "paper_type": "findings", @@ -35279,7 +36711,11 @@ ], "id": "P186", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability", + "reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.218.pdf", "paper_type": "Main-Poster", @@ -35312,7 +36748,10 @@ ], "id": "P1867", "is_paper": true, - "keywords": [], + "keywords": [ + "graph-based methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.765.pdf", "paper_type": "Main-Poster", @@ -35346,7 +36785,10 @@ ], "id": "P187", "is_paper": true, - "keywords": [], + "keywords": [ + "data augmentation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.466.pdf", "paper_type": "findings", @@ -35380,7 +36822,11 @@ ], "id": "P1876", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-word expressions", + "paraphrasing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.290.pdf", "paper_type": "findings", @@ -35416,7 +36862,19 @@ ], "id": "P1879", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "evaluation", + "datasets for low resource languages", + "metrics" + ], + "languages": [ + "hindi", + "gujarati", + "marathi", + "tamil", + "malayalam" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.795.pdf", "paper_type": "Main-Poster", @@ -35451,7 +36909,11 @@ ], "id": "P1880", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training", + "security and privacy" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.399.pdf", "paper_type": "Main-Poster", @@ -35483,7 +36945,10 @@ ], "id": "P1886", "is_paper": true, - "keywords": [], + "keywords": [ + "argument quality assessment" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.778.pdf", "paper_type": "Main-Poster", @@ -35516,7 +36981,10 @@ ], "id": "P1890", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.825.pdf", "paper_type": "findings", @@ -35549,7 +37017,10 @@ ], "id": "P1897", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.670.pdf", "paper_type": "Main-Poster", @@ -35580,7 +37051,15 @@ ], "id": "P1915", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer", + "multilingual benchmarks", + "multilingual evaluation" + ], + "languages": [ + "chinese", + "japanese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.93.pdf", "paper_type": "Main-Oral", @@ -35614,7 +37093,10 @@ ], "id": "P1916", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodal qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.614.pdf", "paper_type": "Main-Poster", @@ -35647,7 +37129,10 @@ ], "id": "P1917", "is_paper": true, - "keywords": [], + "keywords": [ + "legal nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.777.pdf", "paper_type": "Main-Poster", @@ -35678,7 +37163,10 @@ ], "id": "P1922", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training for mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.222.pdf", "paper_type": "findings", @@ -35711,7 +37199,11 @@ ], "id": "P1924", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training", + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.267.pdf", "paper_type": "Main-Oral", @@ -35747,7 +37239,12 @@ ], "id": "P1925", "is_paper": true, - "keywords": [], + "keywords": [ + "commonsense qa", + "knowledge base qa", + "open-domain qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.750.pdf", "paper_type": "Main-Poster", @@ -35782,7 +37279,10 @@ ], "id": "P1934", "is_paper": true, - "keywords": [], + "keywords": [ + "domain adaptation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.177.pdf", "paper_type": "findings", @@ -35815,7 +37315,10 @@ ], "id": "P1936", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.796.pdf", "paper_type": "findings", @@ -35853,7 +37356,11 @@ ], "id": "P1937", "is_paper": true, - "keywords": [], + "keywords": [ + "transfer learning / domain adaptation", + "model compression methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.759.pdf", "paper_type": "findings", @@ -35884,7 +37391,10 @@ ], "id": "P1939", "is_paper": true, - "keywords": [], + "keywords": [ + "generalization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.108.pdf", "paper_type": "findings", @@ -35921,7 +37431,10 @@ ], "id": "P194", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.581.pdf", "paper_type": "findings", @@ -35953,7 +37466,10 @@ ], "id": "P1940", "is_paper": true, - "keywords": [], + "keywords": [ + "reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.742.pdf", "paper_type": "Main-Oral", @@ -35989,7 +37505,12 @@ ], "id": "P1952", "is_paper": true, - "keywords": [], + "keywords": [ + "reading comprehension" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.671.pdf", "paper_type": "findings", @@ -36024,7 +37545,12 @@ ], "id": "P1959", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "interpretability/analysis", + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.574.pdf", "paper_type": "findings", @@ -36062,7 +37588,12 @@ ], "id": "P1969", "is_paper": true, - "keywords": [], + "keywords": [ + "data-to-text generation" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.168.pdf", "paper_type": "Main-Poster", @@ -36096,7 +37627,10 @@ ], "id": "P197", "is_paper": true, - "keywords": [], + "keywords": [ + "human factors in nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.54.pdf", "paper_type": "Main-Poster", @@ -36132,7 +37666,12 @@ ], "id": "P1970", "is_paper": true, - "keywords": [], + "keywords": [ + "reading comprehension", + "multihop qa", + "table qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.147.pdf", "paper_type": "Main-Poster", @@ -36167,7 +37706,10 @@ ], "id": "P1977", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.557.pdf", "paper_type": "findings", @@ -36202,7 +37744,11 @@ ], "id": "P1980", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.385.pdf", "paper_type": "Main-Poster", @@ -36235,7 +37781,11 @@ ], "id": "P1985", "is_paper": true, - "keywords": [], + "keywords": [ + "reading comprehension", + "question generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.870.pdf", "paper_type": "findings", @@ -36270,7 +37820,10 @@ ], "id": "P1996", "is_paper": true, - "keywords": [], + "keywords": [ + "counterfactual/contrastive explanations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.606.pdf", "paper_type": "findings", @@ -36308,7 +37861,10 @@ ], "id": "P2006", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.816.pdf", "paper_type": "Main-Poster", @@ -36342,7 +37898,10 @@ ], "id": "P2012", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.361.pdf", "paper_type": "Main-Poster", @@ -36373,7 +37932,10 @@ ], "id": "P2018", "is_paper": true, - "keywords": [], + "keywords": [ + "computational psycholinguistics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.14.pdf", "paper_type": "Main-Poster", @@ -36409,7 +37971,10 @@ ], "id": "P202", "is_paper": true, - "keywords": [], + "keywords": [ + "semantic parsing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.726.pdf", "paper_type": "findings", @@ -36441,7 +38006,10 @@ ], "id": "P2021", "is_paper": true, - "keywords": [], + "keywords": [ + "linguistic theories" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.429.pdf", "paper_type": "findings", @@ -36473,7 +38041,10 @@ ], "id": "P2023", "is_paper": true, - "keywords": [], + "keywords": [ + "compositionality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.157.pdf", "paper_type": "Main-Oral", @@ -36508,7 +38079,10 @@ ], "id": "P2024", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.287.pdf", "paper_type": "findings", @@ -36542,7 +38116,10 @@ ], "id": "P203", "is_paper": true, - "keywords": [], + "keywords": [ + "argument quality assessment" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.238.pdf", "paper_type": "Main-Poster", @@ -36576,7 +38153,10 @@ ], "id": "P2033", "is_paper": true, - "keywords": [], + "keywords": [ + "methodology" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.60.pdf", "paper_type": "Main-Poster", @@ -36607,7 +38187,10 @@ ], "id": "P2036", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.567.pdf", "paper_type": "Main-Oral", @@ -36641,7 +38224,10 @@ ], "id": "P2037", "is_paper": true, - "keywords": [], + "keywords": [ + "fact checking, rumour/misinformation detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.37.pdf", "paper_type": "Main-Poster", @@ -36675,7 +38261,14 @@ ], "id": "P2039", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation" + ], + "languages": [ + "spanish", + "german", + "japanese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.272.pdf", "paper_type": "findings", @@ -36712,7 +38305,11 @@ ], "id": "P2046", "is_paper": true, - "keywords": [], + "keywords": [ + "reasoning", + "math qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.245.pdf", "paper_type": "Main-Poster", @@ -36746,7 +38343,10 @@ ], "id": "P2048", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-task learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.476.pdf", "paper_type": "findings", @@ -36781,7 +38381,10 @@ ], "id": "P2055", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.702.pdf", "paper_type": "Main-Oral", @@ -36813,7 +38416,10 @@ ], "id": "P2056", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-modal dialogue systems" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.121.pdf", "paper_type": "Main-Poster", @@ -36846,7 +38452,12 @@ ], "id": "P2057", "is_paper": true, - "keywords": [], + "keywords": [ + "applications", + "grounded dialog", + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.25.pdf", "paper_type": "findings", @@ -36879,7 +38490,10 @@ ], "id": "P2058", "is_paper": true, - "keywords": [], + "keywords": [ + "hardness of samples" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.829.pdf", "paper_type": "Main-Poster", @@ -36910,7 +38524,10 @@ ], "id": "P2059", "is_paper": true, - "keywords": [], + "keywords": [ + "speech translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.251.pdf", "paper_type": "Main-Poster", @@ -36941,7 +38558,11 @@ ], "id": "P206", "is_paper": true, - "keywords": [], + "keywords": [ + "discourse relations", + "discourse parsing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.874.pdf", "paper_type": "Main-Poster", @@ -36973,7 +38594,10 @@ ], "id": "P2061", "is_paper": true, - "keywords": [], + "keywords": [ + "financial/business nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.152.pdf", "paper_type": "Main-Poster", @@ -37004,7 +38628,10 @@ ], "id": "P2067", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.126.pdf", "paper_type": "Main-Poster", @@ -37038,7 +38665,11 @@ ], "id": "P2068", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation", + "architectures" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.151.pdf", "paper_type": "Main-Poster", @@ -37070,7 +38701,10 @@ ], "id": "P2071", "is_paper": true, - "keywords": [], + "keywords": [ + "domain adaptation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.130.pdf", "paper_type": "Main-Poster", @@ -37101,7 +38735,15 @@ ], "id": "P2079", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [ + "german", + "french", + "turkish", + "quechua" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.699.pdf", "paper_type": "Main-Poster", @@ -37134,7 +38776,10 @@ ], "id": "P2080", "is_paper": true, - "keywords": [], + "keywords": [ + "contrastive learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.771.pdf", "paper_type": "findings", @@ -37170,7 +38815,10 @@ ], "id": "P2086", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.147.pdf", "paper_type": "Main-Poster", @@ -37201,7 +38849,10 @@ ], "id": "P2091", "is_paper": true, - "keywords": [], + "keywords": [ + "bias/toxicity" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.163.pdf", "paper_type": "Main-Poster", @@ -37233,7 +38884,10 @@ ], "id": "P2092", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.360.pdf", "paper_type": "findings", @@ -37269,7 +38923,10 @@ ], "id": "P2093", "is_paper": true, - "keywords": [], + "keywords": [ + "question generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.20.pdf", "paper_type": "Main-Poster", @@ -37302,7 +38959,10 @@ ], "id": "P2095", "is_paper": true, - "keywords": [], + "keywords": [ + "word embeddings" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.165.pdf", "paper_type": "findings", @@ -37334,7 +38994,10 @@ ], "id": "P2098", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.467.pdf", "paper_type": "findings", @@ -37367,7 +39030,10 @@ ], "id": "P21", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.533.pdf", "paper_type": "Main-Poster", @@ -37403,7 +39069,10 @@ ], "id": "P2105", "is_paper": true, - "keywords": [], + "keywords": [ + "document representation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.14.pdf", "paper_type": "findings", @@ -37436,7 +39105,10 @@ ], "id": "P2106", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.343.pdf", "paper_type": "findings", @@ -37469,7 +39141,12 @@ ], "id": "P211", "is_paper": true, - "keywords": [], + "keywords": [ + "self-supervised learning", + "transfer learning / domain adaptation", + "representation learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.682.pdf", "paper_type": "Main-Poster", @@ -37502,7 +39179,10 @@ ], "id": "P2112", "is_paper": true, - "keywords": [], + "keywords": [ + "word embeddings" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.550.pdf", "paper_type": "findings", @@ -37535,7 +39215,10 @@ ], "id": "P2114", "is_paper": true, - "keywords": [], + "keywords": [ + "lexical relationships" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.260.pdf", "paper_type": "Main-Oral", @@ -37566,7 +39249,10 @@ ], "id": "P2116", "is_paper": true, - "keywords": [], + "keywords": [ + "metaphor" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.58.pdf", "paper_type": "Main-Oral", @@ -37601,7 +39287,10 @@ ], "id": "P2119", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge base qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.576.pdf", "paper_type": "Main-Poster", @@ -37637,7 +39326,10 @@ ], "id": "P2122", "is_paper": true, - "keywords": [], + "keywords": [ + "generative models" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.580.pdf", "paper_type": "Main-Poster", @@ -37670,7 +39362,10 @@ ], "id": "P2123", "is_paper": true, - "keywords": [], + "keywords": [ + "vision question answering" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.569.pdf", "paper_type": "Main-Oral", @@ -37703,7 +39398,10 @@ ], "id": "P2125", "is_paper": true, - "keywords": [], + "keywords": [ + "contrastive learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.100.pdf", "paper_type": "Main-Poster", @@ -37738,7 +39436,10 @@ ], "id": "P2126", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.691.pdf", "paper_type": "Main-Poster", @@ -37771,7 +39472,10 @@ ], "id": "P2127", "is_paper": true, - "keywords": [], + "keywords": [ + "calibration/uncertainty" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.3.pdf", "paper_type": "findings", @@ -37803,7 +39507,10 @@ ], "id": "P2129", "is_paper": true, - "keywords": [], + "keywords": [ + "representation learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.523.pdf", "paper_type": "Main-Poster", @@ -37835,7 +39542,10 @@ ], "id": "P2130", "is_paper": true, - "keywords": [], + "keywords": [ + "lexical semantic change" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.520.pdf", "paper_type": "Main-Oral", @@ -37876,7 +39586,11 @@ ], "id": "P2131", "is_paper": true, - "keywords": [], + "keywords": [ + "right for the wrong reasons", + "ai hype & expectations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.697.pdf", "paper_type": "Main-Oral", @@ -37910,7 +39624,10 @@ ], "id": "P2132", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.211.pdf", "paper_type": "Main-Oral", @@ -37950,7 +39667,10 @@ ], "id": "P2133", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodal qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.660.pdf", "paper_type": "findings", @@ -37987,7 +39707,13 @@ ], "id": "P2134", "is_paper": true, - "keywords": [], + "keywords": [ + "spoken language translation" + ], + "languages": [ + "spanish; castilian", + "german" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.695.pdf", "paper_type": "Main-Poster", @@ -38019,7 +39745,10 @@ ], "id": "P2136", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.306.pdf", "paper_type": "Main-Poster", @@ -38051,7 +39780,10 @@ ], "id": "P2140", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodal applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.890.pdf", "paper_type": "Main-Poster", @@ -38082,7 +39814,10 @@ ], "id": "P2145", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.403.pdf", "paper_type": "findings", @@ -38114,7 +39849,12 @@ ], "id": "P2152", "is_paper": true, - "keywords": [], + "keywords": [ + "reasoning", + "paraphrase generation", + "text simplification" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.762.pdf", "paper_type": "Main-Poster", @@ -38147,7 +39887,12 @@ ], "id": "P2153", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/unfairness mitigation" + ], + "languages": [ + "german" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.246.pdf", "paper_type": "Main-Poster", @@ -38181,7 +39926,10 @@ ], "id": "P2154", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.151.pdf", "paper_type": "Main-Poster", @@ -38214,7 +39962,10 @@ ], "id": "P2158", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-modal dialogue systems" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.189.pdf", "paper_type": "Main-Poster", @@ -38253,7 +40004,10 @@ ], "id": "P2160", "is_paper": true, - "keywords": [], + "keywords": [ + "spoken language translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.872.pdf", "paper_type": "Main-Poster", @@ -38289,7 +40043,10 @@ ], "id": "P2164", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.378.pdf", "paper_type": "Main-Poster", @@ -38320,7 +40077,15 @@ ], "id": "P2165", "is_paper": true, - "keywords": [], + "keywords": [ + "human evaluation", + "automatic evaluation", + "analysis", + "model architectures" + ], + "languages": [ + "german" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.406.pdf", "paper_type": "Main-Oral", @@ -38352,7 +40117,10 @@ ], "id": "P2166", "is_paper": true, - "keywords": [], + "keywords": [ + "mutlilingual representations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.426.pdf", "paper_type": "Main-Poster", @@ -38385,7 +40153,10 @@ ], "id": "P2178", "is_paper": true, - "keywords": [], + "keywords": [ + "legal nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.301.pdf", "paper_type": "findings", @@ -38425,7 +40196,10 @@ ], "id": "P2181", "is_paper": true, - "keywords": [], + "keywords": [ + "interactive and collaborative generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.910.pdf", "paper_type": "Main-Poster", @@ -38457,7 +40231,10 @@ ], "id": "P2186", "is_paper": true, - "keywords": [], + "keywords": [ + "linguistic theories" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.14.pdf", "paper_type": "Main-Oral", @@ -38490,7 +40267,10 @@ ], "id": "P2187", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.98.pdf", "paper_type": "findings", @@ -38525,7 +40305,10 @@ ], "id": "P2192", "is_paper": true, - "keywords": [], + "keywords": [ + "human evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.382.pdf", "paper_type": "Main-Oral", @@ -38561,7 +40344,10 @@ ], "id": "P2197", "is_paper": true, - "keywords": [], + "keywords": [ + "security and privacy" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.719.pdf", "paper_type": "findings", @@ -38595,7 +40381,10 @@ ], "id": "P2200", "is_paper": true, - "keywords": [], + "keywords": [ + "mathematical nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.32.pdf", "paper_type": "Main-Poster", @@ -38629,7 +40418,11 @@ ], "id": "P2201", "is_paper": true, - "keywords": [], + "keywords": [ + "query-focused summarization", + "multi-document summarization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.110.pdf", "paper_type": "Main-Poster", @@ -38665,7 +40458,11 @@ ], "id": "P2204", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.273.pdf", "paper_type": "findings", @@ -38699,7 +40496,10 @@ ], "id": "P2205", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.185.pdf", "paper_type": "Main-Poster", @@ -38733,7 +40533,10 @@ ], "id": "P2209", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.552.pdf", "paper_type": "Main-Poster", @@ -38768,7 +40571,10 @@ ], "id": "P2221", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.453.pdf", "paper_type": "findings", @@ -38803,7 +40609,13 @@ ], "id": "P2222", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation", + "few-shot/zero-shot mt", + "human evaluation", + "pre-training for mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.859.pdf", "paper_type": "Main-Oral", @@ -38834,7 +40646,14 @@ ], "id": "P2223", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual benchmarks", + "multilingual evaluation" + ], + "languages": [ + "arabic", + "korean" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.389.pdf", "paper_type": "findings", @@ -38867,7 +40686,10 @@ ], "id": "P2226", "is_paper": true, - "keywords": [], + "keywords": [ + "hate-speech detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.17.pdf", "paper_type": "Main-Poster", @@ -38900,7 +40722,12 @@ ], "id": "P2229", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets", + "evaluation methodologies", + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.307.pdf", "paper_type": "Main-Oral", @@ -38932,7 +40759,11 @@ ], "id": "P2232", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation", + "factuality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.78.pdf", "paper_type": "Main-Poster", @@ -38965,7 +40796,13 @@ ], "id": "P2238", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation", + "model bias/unfairness mitigation", + "ethical considerations in nlp applications", + "transparency" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.280.pdf", "paper_type": "findings", @@ -38998,7 +40835,10 @@ ], "id": "P2240", "is_paper": true, - "keywords": [], + "keywords": [ + "semantic textual similarity" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.783.pdf", "paper_type": "findings", @@ -39037,7 +40877,10 @@ ], "id": "P2243", "is_paper": true, - "keywords": [], + "keywords": [ + "model compression methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.580.pdf", "paper_type": "findings", @@ -39070,7 +40913,11 @@ ], "id": "P2244", "is_paper": true, - "keywords": [], + "keywords": [ + "stance detection", + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.497.pdf", "paper_type": "Main-Oral", @@ -39102,7 +40949,10 @@ ], "id": "P2247", "is_paper": true, - "keywords": [], + "keywords": [ + "stance detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.560.pdf", "paper_type": "Main-Oral", @@ -39137,7 +40987,10 @@ ], "id": "P2251", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.249.pdf", "paper_type": "findings", @@ -39171,7 +41024,10 @@ ], "id": "P2258", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.10.pdf", "paper_type": "Main-Oral", @@ -39205,7 +41061,10 @@ ], "id": "P2261", "is_paper": true, - "keywords": [], + "keywords": [ + "lexical relationships" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.308.pdf", "paper_type": "Main-Poster", @@ -39239,7 +41098,10 @@ ], "id": "P2262", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.869.pdf", "paper_type": "Main-Oral", @@ -39273,7 +41135,12 @@ ], "id": "P2269", "is_paper": true, - "keywords": [], + "keywords": [ + "passage retrieval", + "dense retrieval", + "document representation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.167.pdf", "paper_type": "findings", @@ -39304,7 +41171,10 @@ ], "id": "P2270", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.342.pdf", "paper_type": "findings", @@ -39335,7 +41205,10 @@ ], "id": "P2272", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot/zero-shot mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.892.pdf", "paper_type": "findings", @@ -39366,7 +41239,10 @@ ], "id": "P2273", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.46.pdf", "paper_type": "Main-Poster", @@ -39402,7 +41278,10 @@ ], "id": "P2274", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.440.pdf", "paper_type": "findings", @@ -39434,7 +41313,10 @@ ], "id": "P228", "is_paper": true, - "keywords": [], + "keywords": [ + "stance detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.240.pdf", "paper_type": "Main-Poster", @@ -39469,7 +41351,10 @@ ], "id": "P2283", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.548.pdf", "paper_type": "Main-Poster", @@ -39501,7 +41386,26 @@ ], "id": "P2284", "is_paper": true, - "keywords": [], + "keywords": [ + "benchmarking", + "language resources", + "multilingual corpora", + "nlp datasets", + "automatic evaluation of datasets" + ], + "languages": [ + "russian", + "italian", + "urdu", + "japanese", + "spanish", + "danish", + "brazilian portuguese", + "french", + "german", + "slovene", + "basque" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.269.pdf", "paper_type": "Main-Poster", @@ -39533,7 +41437,10 @@ ], "id": "P2288", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.789.pdf", "paper_type": "findings", @@ -39568,7 +41475,10 @@ ], "id": "P2289", "is_paper": true, - "keywords": [], + "keywords": [ + "parameter-efficient finetuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.539.pdf", "paper_type": "findings", @@ -39604,7 +41514,22 @@ ], "id": "P2291", "is_paper": true, - "keywords": [], + "keywords": [ + "code-switching", + "multilingualism", + "cross-lingual transfer", + "mutlilingual representations", + "multilingual evaluation" + ], + "languages": [ + "spanish-english codemixing", + "hindi-english codemixing", + "spanish", + "french", + "german", + "hindi", + "thai" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.199.pdf", "paper_type": "Main-Poster", @@ -39638,7 +41563,10 @@ ], "id": "P2296", "is_paper": true, - "keywords": [], + "keywords": [ + "generalization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.179.pdf", "paper_type": "Main-Poster", @@ -39671,7 +41599,10 @@ ], "id": "P2297", "is_paper": true, - "keywords": [], + "keywords": [ + "inference methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.636.pdf", "paper_type": "findings", @@ -39703,7 +41634,10 @@ ], "id": "P2298", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.631.pdf", "paper_type": "findings", @@ -39740,7 +41674,10 @@ ], "id": "P23", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.572.pdf", "paper_type": "findings", @@ -39776,7 +41713,10 @@ ], "id": "P230", "is_paper": true, - "keywords": [], + "keywords": [ + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.264.pdf", "paper_type": "Main-Poster", @@ -39807,7 +41747,10 @@ ], "id": "P2305", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/unfairness mitigation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.490.pdf", "paper_type": "Main-Poster", @@ -39839,7 +41782,11 @@ ], "id": "P2307", "is_paper": true, - "keywords": [], + "keywords": [ + "semantic parsing", + "generalization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.78.pdf", "paper_type": "Main-Poster", @@ -39872,7 +41819,10 @@ ], "id": "P2311", "is_paper": true, - "keywords": [], + "keywords": [ + "human-in-the-loop / active learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.658.pdf", "paper_type": "findings", @@ -39907,7 +41857,10 @@ ], "id": "P2317", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.51.pdf", "paper_type": "Main-Oral", @@ -39939,7 +41892,12 @@ ], "id": "P2320", "is_paper": true, - "keywords": [], + "keywords": [ + "data shortcuts/artifacts", + "hardness of samples", + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.833.pdf", "paper_type": "findings", @@ -39971,7 +41929,10 @@ ], "id": "P2321", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.865.pdf", "paper_type": "findings", @@ -40005,7 +41966,10 @@ ], "id": "P2325", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.429.pdf", "paper_type": "Main-Poster", @@ -40041,7 +42005,10 @@ ], "id": "P233", "is_paper": true, - "keywords": [], + "keywords": [ + "data-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.56.pdf", "paper_type": "Main-Poster", @@ -40082,7 +42049,10 @@ ], "id": "P2330", "is_paper": true, - "keywords": [], + "keywords": [ + "code generation and understanding" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.9.pdf", "paper_type": "Main-Poster", @@ -40117,7 +42087,11 @@ ], "id": "P2331", "is_paper": true, - "keywords": [], + "keywords": [ + "linguistic variation", + "dialects and language varieties" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.44.pdf", "paper_type": "Main-Poster", @@ -40150,7 +42124,14 @@ ], "id": "P2333", "is_paper": true, - "keywords": [], + "keywords": [ + "less-resourced languages", + "indigenous languages", + "resources for less-resourced languages" + ], + "languages": [ + "assamese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.739.pdf", "paper_type": "findings", @@ -40190,7 +42171,10 @@ ], "id": "P2336", "is_paper": true, - "keywords": [], + "keywords": [ + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.97.pdf", "paper_type": "Main-Oral", @@ -40224,7 +42208,10 @@ ], "id": "P2339", "is_paper": true, - "keywords": [], + "keywords": [ + "mutlilingual representations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.673.pdf", "paper_type": "Main-Poster", @@ -40260,7 +42247,11 @@ ], "id": "P2343", "is_paper": true, - "keywords": [], + "keywords": [ + "hate-speech detection", + "sociolinguistics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.392.pdf", "paper_type": "findings", @@ -40296,7 +42287,11 @@ ], "id": "P2349", "is_paper": true, - "keywords": [], + "keywords": [ + "graph-based methods", + "self-supervised learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.387.pdf", "paper_type": "Main-Oral", @@ -40332,7 +42327,10 @@ ], "id": "P2350", "is_paper": true, - "keywords": [], + "keywords": [ + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.502.pdf", "paper_type": "Main-Poster", @@ -40366,7 +42364,10 @@ ], "id": "P2353", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal pretraining" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.43.pdf", "paper_type": "Main-Poster", @@ -40401,7 +42402,10 @@ ], "id": "P2354", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.495.pdf", "paper_type": "Main-Poster", @@ -40432,7 +42436,11 @@ ], "id": "P2356", "is_paper": true, - "keywords": [], + "keywords": [ + "probing", + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.629.pdf", "paper_type": "Main-Poster", @@ -40463,7 +42471,10 @@ ], "id": "P2358", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.113.pdf", "paper_type": "Main-Poster", @@ -40499,6 +42510,7 @@ "id": "P236", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.664.pdf", "paper_type": "Main-Poster", @@ -40534,7 +42546,10 @@ ], "id": "P2364", "is_paper": true, - "keywords": [], + "keywords": [ + "semantic parsing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.470.pdf", "paper_type": "Main-Poster", @@ -40565,7 +42580,10 @@ ], "id": "P2367", "is_paper": true, - "keywords": [], + "keywords": [ + "compositionality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.38.pdf", "paper_type": "Main-Oral", @@ -40599,7 +42617,10 @@ ], "id": "P2373", "is_paper": true, - "keywords": [], + "keywords": [ + "free-text/natural language explanations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.47.pdf", "paper_type": "Main-Oral", @@ -40634,7 +42655,10 @@ ], "id": "P2379", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.763.pdf", "paper_type": "Main-Poster", @@ -40668,7 +42692,10 @@ ], "id": "P2383", "is_paper": true, - "keywords": [], + "keywords": [ + "coreference resolution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.100.pdf", "paper_type": "findings", @@ -40702,7 +42729,10 @@ ], "id": "P2388", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.155.pdf", "paper_type": "Main-Poster", @@ -40734,7 +42764,10 @@ ], "id": "P2391", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal pretraining" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.29.pdf", "paper_type": "Main-Poster", @@ -40768,7 +42801,11 @@ ], "id": "P2393", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic speech recognition", + "spoken language translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.483.pdf", "paper_type": "findings", @@ -40802,7 +42839,10 @@ ], "id": "P2396", "is_paper": true, - "keywords": [], + "keywords": [ + "calibration/uncertainty" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.75.pdf", "paper_type": "Main-Poster", @@ -40838,7 +42878,10 @@ ], "id": "P2401", "is_paper": true, - "keywords": [], + "keywords": [ + "hate speech detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.22.pdf", "paper_type": "Main-Poster", @@ -40872,7 +42915,10 @@ ], "id": "P2404", "is_paper": true, - "keywords": [], + "keywords": [ + "model compression methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.5.pdf", "paper_type": "Main-Oral", @@ -40906,7 +42952,10 @@ ], "id": "P2406", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.196.pdf", "paper_type": "findings", @@ -40939,7 +42988,10 @@ ], "id": "P2407", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.282.pdf", "paper_type": "findings", @@ -40973,7 +43025,10 @@ ], "id": "P2410", "is_paper": true, - "keywords": [], + "keywords": [ + "topic modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.145.pdf", "paper_type": "Main-Poster", @@ -41009,7 +43064,10 @@ ], "id": "P2411", "is_paper": true, - "keywords": [], + "keywords": [ + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.875.pdf", "paper_type": "findings", @@ -41041,7 +43099,10 @@ ], "id": "P2412", "is_paper": true, - "keywords": [], + "keywords": [ + "free-text/natural language explanations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.707.pdf", "paper_type": "Main-Poster", @@ -41075,7 +43136,10 @@ ], "id": "P2416", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.528.pdf", "paper_type": "findings", @@ -41108,7 +43172,10 @@ ], "id": "P2419", "is_paper": true, - "keywords": [], + "keywords": [ + "reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.321.pdf", "paper_type": "findings", @@ -41140,7 +43207,10 @@ ], "id": "P2421", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.375.pdf", "paper_type": "Main-Oral", @@ -41175,7 +43245,10 @@ ], "id": "P2422", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.657.pdf", "paper_type": "findings", @@ -41208,7 +43281,10 @@ ], "id": "P2423", "is_paper": true, - "keywords": [], + "keywords": [ + "semantic parsing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.887.pdf", "paper_type": "Main-Poster", @@ -41242,7 +43318,10 @@ ], "id": "P2429", "is_paper": true, - "keywords": [], + "keywords": [ + "negative results" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.45.pdf", "paper_type": "findings", @@ -41279,7 +43358,10 @@ ], "id": "P2433", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.183.pdf", "paper_type": "Main-Poster", @@ -41311,7 +43393,10 @@ ], "id": "P2437", "is_paper": true, - "keywords": [], + "keywords": [ + "vision question answering" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.5.pdf", "paper_type": "findings", @@ -41343,7 +43428,13 @@ ], "id": "P2439", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual mt", + "multimodality" + ], + "languages": [ + "german" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.700.pdf", "paper_type": "Main-Poster", @@ -41376,7 +43467,12 @@ ], "id": "P2441", "is_paper": true, - "keywords": [], + "keywords": [ + "counterfactual/contrastive explanations", + "explanation faithfulness", + "feature attribution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.301.pdf", "paper_type": "Main-Poster", @@ -41411,7 +43507,10 @@ ], "id": "P2448", "is_paper": true, - "keywords": [], + "keywords": [ + "model compression methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.111.pdf", "paper_type": "Main-Poster", @@ -41445,7 +43544,10 @@ ], "id": "P2449", "is_paper": true, - "keywords": [], + "keywords": [ + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.877.pdf", "paper_type": "Main-Poster", @@ -41477,7 +43579,21 @@ ], "id": "P245", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer", + "mutlilingual representations" + ], + "languages": [ + "spanish", + "chinese", + "arabic", + "hindi", + "turkish", + "polish", + "japanese", + "portuguese", + "korean" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.296.pdf", "paper_type": "Main-Poster", @@ -41519,7 +43635,10 @@ ], "id": "P2460", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodal applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.748.pdf", "paper_type": "Main-Poster", @@ -41555,7 +43674,10 @@ ], "id": "P2461", "is_paper": true, - "keywords": [], + "keywords": [ + "(non-)generalizability" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.844.pdf", "paper_type": "findings", @@ -41589,7 +43711,11 @@ ], "id": "P2462", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis", + "quantiative analyses of news and/or social media" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.813.pdf", "paper_type": "findings", @@ -41622,7 +43748,10 @@ ], "id": "P2465", "is_paper": true, - "keywords": [], + "keywords": [ + "participatory/community-based nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.701.pdf", "paper_type": "findings", @@ -41659,7 +43788,10 @@ ], "id": "P2466", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.792.pdf", "paper_type": "findings", @@ -41695,7 +43827,10 @@ ], "id": "P2469", "is_paper": true, - "keywords": [], + "keywords": [ + "commonsense reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.630.pdf", "paper_type": "Main-Poster", @@ -41729,7 +43864,12 @@ ], "id": "P2480", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "event extraction", + "document-level extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.863.pdf", "paper_type": "findings", @@ -41765,7 +43905,10 @@ ], "id": "P2484", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal information extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.127.pdf", "paper_type": "findings", @@ -41797,7 +43940,10 @@ ], "id": "P2489", "is_paper": true, - "keywords": [], + "keywords": [ + "entity linking/disambiguation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.850.pdf", "paper_type": "Main-Poster", @@ -41836,7 +43982,12 @@ ], "id": "P249", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal pretraining", + "cross-modal content generation", + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.900.pdf", "paper_type": "Main-Poster", @@ -41870,7 +44021,11 @@ ], "id": "P2492", "is_paper": true, - "keywords": [], + "keywords": [ + "image text matching", + "vision question answering" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.909.pdf", "paper_type": "Main-Poster", @@ -41906,7 +44061,10 @@ ], "id": "P25", "is_paper": true, - "keywords": [], + "keywords": [ + "vision language navigation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.190.pdf", "paper_type": "findings", @@ -41938,7 +44096,10 @@ ], "id": "P250", "is_paper": true, - "keywords": [], + "keywords": [ + "retrieval-augmented generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.26.pdf", "paper_type": "Main-Oral", @@ -41969,7 +44130,10 @@ ], "id": "P2506", "is_paper": true, - "keywords": [], + "keywords": [ + "ai hype & expectations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.37.pdf", "paper_type": "Main-Oral", @@ -42001,7 +44165,12 @@ ], "id": "P2507", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.77.pdf", "paper_type": "Main-Poster", @@ -42033,7 +44202,10 @@ ], "id": "P2511", "is_paper": true, - "keywords": [], + "keywords": [ + "probing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.70.pdf", "paper_type": "Main-Poster", @@ -42068,7 +44240,11 @@ ], "id": "P2512", "is_paper": true, - "keywords": [], + "keywords": [ + "vision question answering", + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.601.pdf", "paper_type": "Main-Poster", @@ -42101,7 +44277,10 @@ ], "id": "P2519", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.225.pdf", "paper_type": "Main-Poster", @@ -42137,7 +44316,10 @@ ], "id": "P2521", "is_paper": true, - "keywords": [], + "keywords": [ + "fact checking, rumour/misinformation detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.386.pdf", "paper_type": "Main-Poster", @@ -42170,7 +44352,10 @@ ], "id": "P2522", "is_paper": true, - "keywords": [], + "keywords": [ + "structured prediction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.605.pdf", "paper_type": "Main-Oral", @@ -42200,7 +44385,12 @@ ], "id": "P2525", "is_paper": true, - "keywords": [], + "keywords": [ + "morphological analysis" + ], + "languages": [ + "japanese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.2.pdf", "paper_type": "Main-Poster", @@ -42234,7 +44424,11 @@ ], "id": "P2526", "is_paper": true, - "keywords": [], + "keywords": [ + "generative models", + "optimization methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.192.pdf", "paper_type": "findings", @@ -42268,7 +44462,11 @@ ], "id": "P2529", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal pretraining", + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.27.pdf", "paper_type": "findings", @@ -42302,7 +44500,10 @@ ], "id": "P2530", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.573.pdf", "paper_type": "Main-Poster", @@ -42341,7 +44542,10 @@ ], "id": "P2531", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.875.pdf", "paper_type": "Main-Poster", @@ -42373,7 +44577,10 @@ ], "id": "P2533", "is_paper": true, - "keywords": [], + "keywords": [ + "vision language navigation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.227.pdf", "paper_type": "findings", @@ -42407,7 +44614,10 @@ ], "id": "P2534", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/unfairness mitigation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.30.pdf", "paper_type": "Main-Poster", @@ -42445,7 +44655,10 @@ ], "id": "P2536", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.9.pdf", "paper_type": "findings", @@ -42479,7 +44692,11 @@ ], "id": "P2538", "is_paper": true, - "keywords": [], + "keywords": [ + "argument mining", + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.821.pdf", "paper_type": "findings", @@ -42511,7 +44728,10 @@ ], "id": "P254", "is_paper": true, - "keywords": [], + "keywords": [ + "open information extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.63.pdf", "paper_type": "Main-Poster", @@ -42543,7 +44763,12 @@ ], "id": "P2547", "is_paper": true, - "keywords": [], + "keywords": [ + "inference methods" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.792.pdf", "paper_type": "Main-Poster", @@ -42580,7 +44805,10 @@ ], "id": "P255", "is_paper": true, - "keywords": [], + "keywords": [ + "graph-based methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.597.pdf", "paper_type": "Main-Poster", @@ -42616,6 +44844,7 @@ "id": "P2550", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.780.pdf", "paper_type": "Main-Poster", @@ -42646,7 +44875,12 @@ ], "id": "P2562", "is_paper": true, - "keywords": [], + "keywords": [ + "free-text/natural language explanations" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.532.pdf", "paper_type": "findings", @@ -42679,7 +44913,10 @@ ], "id": "P2565", "is_paper": true, - "keywords": [], + "keywords": [ + "model compression methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.738.pdf", "paper_type": "Main-Oral", @@ -42712,7 +44949,11 @@ ], "id": "P2576", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented", + "multilingual / low resource" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.480.pdf", "paper_type": "Main-Poster", @@ -42747,7 +44988,10 @@ ], "id": "P2577", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.820.pdf", "paper_type": "Main-Oral", @@ -42787,7 +45031,11 @@ ], "id": "P258", "is_paper": true, - "keywords": [], + "keywords": [ + "graph-based methods", + "representation learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.349.pdf", "paper_type": "Main-Poster", @@ -42819,7 +45067,11 @@ ], "id": "P2582", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation", + "factuality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.402.pdf", "paper_type": "findings", @@ -42855,7 +45107,11 @@ ], "id": "P2587", "is_paper": true, - "keywords": [], + "keywords": [ + "applications", + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.158.pdf", "paper_type": "Main-Oral", @@ -42893,7 +45149,10 @@ ], "id": "P2589", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.331.pdf", "paper_type": "Main-Poster", @@ -42924,7 +45183,10 @@ ], "id": "P2592", "is_paper": true, - "keywords": [], + "keywords": [ + "word embeddings" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.221.pdf", "paper_type": "findings", @@ -42959,7 +45221,11 @@ ], "id": "P2596", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction", + "document-level extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.17.pdf", "paper_type": "Main-Poster", @@ -42992,7 +45258,10 @@ ], "id": "P2602", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.350.pdf", "paper_type": "Main-Oral", @@ -43024,7 +45293,10 @@ ], "id": "P2606", "is_paper": true, - "keywords": [], + "keywords": [ + "model architectures" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.647.pdf", "paper_type": "Main-Poster", @@ -43056,7 +45328,10 @@ ], "id": "P2609", "is_paper": true, - "keywords": [], + "keywords": [ + "model compression methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.463.pdf", "paper_type": "findings", @@ -43089,7 +45364,11 @@ ], "id": "P2616", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation methodologies", + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.881.pdf", "paper_type": "Main-Poster", @@ -43123,7 +45402,10 @@ ], "id": "P2621", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.376.pdf", "paper_type": "Main-Poster", @@ -43158,7 +45440,11 @@ ], "id": "P2622", "is_paper": true, - "keywords": [], + "keywords": [ + "explanation faithfulness", + "free-text/natural language explanations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.304.pdf", "paper_type": "Main-Oral", @@ -43192,7 +45478,13 @@ ], "id": "P2629", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingualism" + ], + "languages": [ + "malay", + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.736.pdf", "paper_type": "findings", @@ -43227,7 +45519,11 @@ ], "id": "P2630", "is_paper": true, - "keywords": [], + "keywords": [ + "multihop qa", + "open-domain qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.89.pdf", "paper_type": "Main-Poster", @@ -43262,7 +45558,10 @@ ], "id": "P2633", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.8.pdf", "paper_type": "Main-Poster", @@ -43294,7 +45593,12 @@ ], "id": "P2634", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingualism", + "cross-lingual transfer", + "multilingual evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.619.pdf", "paper_type": "findings", @@ -43331,7 +45635,10 @@ ], "id": "P2635", "is_paper": true, - "keywords": [], + "keywords": [ + "pronunciation modelling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.145.pdf", "paper_type": "Main-Oral", @@ -43369,7 +45676,10 @@ ], "id": "P2637", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.806.pdf", "paper_type": "findings", @@ -43402,7 +45712,12 @@ ], "id": "P2645", "is_paper": true, - "keywords": [], + "keywords": [ + "benchmarking", + "nlp datasets", + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.31.pdf", "paper_type": "Main-Poster", @@ -43438,7 +45753,11 @@ ], "id": "P2649", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "zero/few-shot extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.855.pdf", "paper_type": "Main-Poster", @@ -43472,7 +45791,10 @@ ], "id": "P2651", "is_paper": true, - "keywords": [], + "keywords": [ + "dialogue state tracking" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.304.pdf", "paper_type": "findings", @@ -43505,7 +45827,10 @@ ], "id": "P2659", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.736.pdf", "paper_type": "Main-Poster", @@ -43537,7 +45862,10 @@ ], "id": "P2660", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.313.pdf", "paper_type": "Main-Poster", @@ -43571,7 +45899,10 @@ ], "id": "P2663", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.283.pdf", "paper_type": "Main-Poster", @@ -43606,7 +45937,11 @@ ], "id": "P2664", "is_paper": true, - "keywords": [], + "keywords": [ + "legal nlp", + "security/privacy" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.740.pdf", "paper_type": "Main-Poster", @@ -43638,7 +45973,11 @@ ], "id": "P2666", "is_paper": true, - "keywords": [], + "keywords": [ + "speech technologies", + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.141.pdf", "paper_type": "Main-Poster", @@ -43671,7 +46010,10 @@ ], "id": "P267", "is_paper": true, - "keywords": [], + "keywords": [ + "discourse relations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.357.pdf", "paper_type": "Main-Poster", @@ -43704,7 +46046,10 @@ ], "id": "P2678", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.583.pdf", "paper_type": "Main-Oral", @@ -43737,7 +46082,10 @@ ], "id": "P2684", "is_paper": true, - "keywords": [], + "keywords": [ + "open-domain qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.159.pdf", "paper_type": "Main-Poster", @@ -43775,7 +46123,10 @@ ], "id": "P2689", "is_paper": true, - "keywords": [], + "keywords": [ + "bias/toxicity" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.123.pdf", "paper_type": "Main-Poster", @@ -43809,7 +46160,10 @@ ], "id": "P2691", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic speech recognition" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.137.pdf", "paper_type": "Main-Poster", @@ -43844,7 +46198,10 @@ ], "id": "P2699", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.550.pdf", "paper_type": "Main-Poster", @@ -43875,7 +46232,10 @@ ], "id": "P2700", "is_paper": true, - "keywords": [], + "keywords": [ + "data shortcuts/artifacts" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.781.pdf", "paper_type": "findings", @@ -43908,7 +46268,10 @@ ], "id": "P2701", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.64.pdf", "paper_type": "Main-Poster", @@ -43945,7 +46308,12 @@ ], "id": "P2703", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation", + "model bias/unfairness mitigation", + "human factors in nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.227.pdf", "paper_type": "Main-Poster", @@ -43982,7 +46350,12 @@ ], "id": "P2706", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training", + "prompting", + "retrieval-augmented models" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.132.pdf", "paper_type": "findings", @@ -44016,7 +46389,10 @@ ], "id": "P2712", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.242.pdf", "paper_type": "findings", @@ -44048,7 +46424,10 @@ ], "id": "P2713", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.485.pdf", "paper_type": "Main-Poster", @@ -44081,7 +46460,10 @@ ], "id": "P2715", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal information extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.77.pdf", "paper_type": "Main-Poster", @@ -44116,7 +46498,12 @@ ], "id": "P2717", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "nlp datasets", + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.257.pdf", "paper_type": "Main-Poster", @@ -44152,7 +46539,10 @@ ], "id": "P2718", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal content generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.85.pdf", "paper_type": "findings", @@ -44188,7 +46578,10 @@ ], "id": "P2722", "is_paper": true, - "keywords": [], + "keywords": [ + "legal nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.193.pdf", "paper_type": "Main-Poster", @@ -44218,7 +46611,10 @@ ], "id": "P2728", "is_paper": true, - "keywords": [], + "keywords": [ + "speech translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.509.pdf", "paper_type": "findings", @@ -44253,7 +46649,10 @@ ], "id": "P273", "is_paper": true, - "keywords": [], + "keywords": [ + "continual learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.347.pdf", "paper_type": "findings", @@ -44292,7 +46691,11 @@ ], "id": "P2730", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "zero/few-shot extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.764.pdf", "paper_type": "Main-Poster", @@ -44326,7 +46729,12 @@ ], "id": "P274", "is_paper": true, - "keywords": [], + "keywords": [ + "self-supervised learning", + "reinforcement learning", + "human-in-the-loop / active learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.697.pdf", "paper_type": "findings", @@ -44365,7 +46773,10 @@ ], "id": "P2740", "is_paper": true, - "keywords": [], + "keywords": [ + "reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.535.pdf", "paper_type": "Main-Poster", @@ -44397,7 +46808,12 @@ ], "id": "P2746", "is_paper": true, - "keywords": [], + "keywords": [ + "code-switching" + ], + "languages": [ + "spanish" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.469.pdf", "paper_type": "findings", @@ -44436,7 +46852,11 @@ ], "id": "P2749", "is_paper": true, - "keywords": [], + "keywords": [ + "model compression methods", + "parameter-efficient finetuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.511.pdf", "paper_type": "findings", @@ -44472,7 +46892,10 @@ ], "id": "P2755", "is_paper": true, - "keywords": [], + "keywords": [ + "transfer learning / domain adaptation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.498.pdf", "paper_type": "Main-Poster", @@ -44503,7 +46926,10 @@ ], "id": "P276", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training for mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.63.pdf", "paper_type": "Main-Poster", @@ -44539,7 +46965,10 @@ ], "id": "P2766", "is_paper": true, - "keywords": [], + "keywords": [ + "grounded dialog" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.608.pdf", "paper_type": "Main-Poster", @@ -44573,7 +47002,10 @@ ], "id": "P2767", "is_paper": true, - "keywords": [], + "keywords": [ + "dialogue state tracking" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.898.pdf", "paper_type": "findings", @@ -44605,7 +47037,12 @@ ], "id": "P2768", "is_paper": true, - "keywords": [], + "keywords": [ + "stance detection" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.747.pdf", "paper_type": "Main-Oral", @@ -44638,7 +47075,10 @@ ], "id": "P2769", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.857.pdf", "paper_type": "Main-Poster", @@ -44672,7 +47112,10 @@ ], "id": "P2772", "is_paper": true, - "keywords": [], + "keywords": [ + "methodology" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.639.pdf", "paper_type": "findings", @@ -44704,7 +47147,10 @@ ], "id": "P2774", "is_paper": true, - "keywords": [], + "keywords": [ + "query-focused summarization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.731.pdf", "paper_type": "Main-Poster", @@ -44741,7 +47187,10 @@ ], "id": "P2776", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.39.pdf", "paper_type": "Main-Poster", @@ -44774,7 +47223,11 @@ ], "id": "P2786", "is_paper": true, - "keywords": [], + "keywords": [ + "parameter-efficient finetuning", + "meta learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.737.pdf", "paper_type": "findings", @@ -44807,7 +47260,10 @@ ], "id": "P2788", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/unfairness mitigation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.369.pdf", "paper_type": "findings", @@ -44840,7 +47296,10 @@ ], "id": "P279", "is_paper": true, - "keywords": [], + "keywords": [ + "speech translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.228.pdf", "paper_type": "findings", @@ -44874,7 +47333,10 @@ ], "id": "P2796", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation and metrics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.896.pdf", "paper_type": "findings", @@ -44905,7 +47367,12 @@ ], "id": "P2797", "is_paper": true, - "keywords": [], + "keywords": [ + "spoken dialogue systems", + "multi-modal dialogue systems", + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.732.pdf", "paper_type": "Main-Poster", @@ -44938,7 +47405,10 @@ ], "id": "P282", "is_paper": true, - "keywords": [], + "keywords": [ + "forgotten lessons" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.160.pdf", "paper_type": "Main-Poster", @@ -44971,7 +47441,10 @@ ], "id": "P2823", "is_paper": true, - "keywords": [], + "keywords": [ + "reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.130.pdf", "paper_type": "Main-Poster", @@ -45007,7 +47480,10 @@ ], "id": "P2824", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.800.pdf", "paper_type": "findings", @@ -45043,7 +47519,11 @@ ], "id": "P2826", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.610.pdf", "paper_type": "Main-Poster", @@ -45091,7 +47571,10 @@ ], "id": "P283", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.891.pdf", "paper_type": "Main-Poster", @@ -45125,7 +47608,10 @@ ], "id": "P2830", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.420.pdf", "paper_type": "findings", @@ -45161,7 +47647,10 @@ ], "id": "P2835", "is_paper": true, - "keywords": [], + "keywords": [ + "speech translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.195.pdf", "paper_type": "findings", @@ -45193,7 +47682,10 @@ ], "id": "P284", "is_paper": true, - "keywords": [], + "keywords": [ + "domain adaptation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.492.pdf", "paper_type": "Main-Poster", @@ -45225,7 +47717,11 @@ ], "id": "P2841", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "zero/few-shot extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.621.pdf", "paper_type": "Main-Poster", @@ -45256,7 +47752,10 @@ ], "id": "P2842", "is_paper": true, - "keywords": [], + "keywords": [ + "dialogue state tracking" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.6.pdf", "paper_type": "Main-Poster", @@ -45292,7 +47791,10 @@ ], "id": "P2848", "is_paper": true, - "keywords": [], + "keywords": [ + "efficient mt training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.73.pdf", "paper_type": "Main-Poster", @@ -45327,6 +47829,7 @@ "id": "P2852", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.271.pdf", "paper_type": "Main-Poster", @@ -45361,7 +47864,10 @@ ], "id": "P2859", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot/zero-shot mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.112.pdf", "paper_type": "Main-Poster", @@ -45398,7 +47904,11 @@ ], "id": "P286", "is_paper": true, - "keywords": [], + "keywords": [ + "vision question answering", + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.41.pdf", "paper_type": "Main-Poster", @@ -45434,7 +47944,10 @@ ], "id": "P2860", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-modal dialogue systems" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.217.pdf", "paper_type": "findings", @@ -45470,7 +47983,12 @@ ], "id": "P2863", "is_paper": true, - "keywords": [], + "keywords": [ + "logical reasoning", + "knowledge base qa", + "generalization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.259.pdf", "paper_type": "Main-Poster", @@ -45505,7 +48023,10 @@ ], "id": "P2866", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial attacks/examples/training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.194.pdf", "paper_type": "Main-Poster", @@ -45538,7 +48059,11 @@ ], "id": "P2868", "is_paper": true, - "keywords": [], + "keywords": [ + "financial/business nlp", + "knowledge graphs" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.119.pdf", "paper_type": "Main-Poster", @@ -45572,7 +48097,10 @@ ], "id": "P2873", "is_paper": true, - "keywords": [], + "keywords": [ + "interactive mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.434.pdf", "paper_type": "Main-Poster", @@ -45604,7 +48132,10 @@ ], "id": "P2874", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.305.pdf", "paper_type": "Main-Oral", @@ -45634,7 +48165,15 @@ ], "id": "P2876", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation", + "ai hype & expectations" + ], + "languages": [ + "german", + "japanese", + "ukrainian" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.381.pdf", "paper_type": "findings", @@ -45668,7 +48207,10 @@ ], "id": "P2877", "is_paper": true, - "keywords": [], + "keywords": [ + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.24.pdf", "paper_type": "Main-Poster", @@ -45700,7 +48242,10 @@ ], "id": "P2884", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge graphs" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.878.pdf", "paper_type": "findings", @@ -45735,7 +48280,10 @@ ], "id": "P2885", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.178.pdf", "paper_type": "findings", @@ -45774,7 +48322,12 @@ ], "id": "P2887", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-task learning", + "data augmentation", + "model compression methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.507.pdf", "paper_type": "findings", @@ -45809,7 +48362,10 @@ ], "id": "P2891", "is_paper": true, - "keywords": [], + "keywords": [ + "cognitive modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.741.pdf", "paper_type": "Main-Poster", @@ -45843,7 +48399,12 @@ ], "id": "P2893", "is_paper": true, - "keywords": [], + "keywords": [ + "speech translation" + ], + "languages": [ + "japanese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.72.pdf", "paper_type": "findings", @@ -45876,7 +48437,10 @@ ], "id": "P2901", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge graphs" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.586.pdf", "paper_type": "Main-Poster", @@ -45912,7 +48476,10 @@ ], "id": "P2907", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.829.pdf", "paper_type": "findings", @@ -45944,7 +48511,10 @@ ], "id": "P2910", "is_paper": true, - "keywords": [], + "keywords": [ + "right for the wrong reasons" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.323.pdf", "paper_type": "Main-Poster", @@ -45977,7 +48547,12 @@ ], "id": "P292", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training", + "interpretability/analysis", + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.756.pdf", "paper_type": "Main-Poster", @@ -46011,7 +48586,10 @@ ], "id": "P2920", "is_paper": true, - "keywords": [], + "keywords": [ + "self-supervised learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.564.pdf", "paper_type": "Main-Poster", @@ -46045,7 +48623,10 @@ ], "id": "P2923", "is_paper": true, - "keywords": [], + "keywords": [ + "passage retrieval" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.366.pdf", "paper_type": "Main-Poster", @@ -46081,7 +48662,12 @@ ], "id": "P2928", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.453.pdf", "paper_type": "Main-Poster", @@ -46114,7 +48700,10 @@ ], "id": "P293", "is_paper": true, - "keywords": [], + "keywords": [ + "frame detection and analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.486.pdf", "paper_type": "Main-Oral", @@ -46148,7 +48737,11 @@ ], "id": "P2934", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/unfairness mitigation", + "ethical considerations in nlp applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.232.pdf", "paper_type": "Main-Poster", @@ -46179,7 +48772,12 @@ ], "id": "P294", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training", + "scaling", + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.142.pdf", "paper_type": "Main-Poster", @@ -46209,7 +48807,10 @@ ], "id": "P2942", "is_paper": true, - "keywords": [], + "keywords": [ + "anaphora resolution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.4.pdf", "paper_type": "Main-Poster", @@ -46242,7 +48843,13 @@ ], "id": "P2943", "is_paper": true, - "keywords": [], + "keywords": [ + "phonology" + ], + "languages": [ + "romance languages", + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.3.pdf", "paper_type": "Main-Oral", @@ -46274,7 +48881,12 @@ ], "id": "P2946", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "automatic creation and evaluation of language resources", + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.162.pdf", "paper_type": "Main-Poster", @@ -46308,7 +48920,10 @@ ], "id": "P295", "is_paper": true, - "keywords": [], + "keywords": [ + "model compression methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.212.pdf", "paper_type": "Main-Oral", @@ -46343,7 +48958,10 @@ ], "id": "P2952", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.327.pdf", "paper_type": "Main-Poster", @@ -46381,7 +48999,10 @@ ], "id": "P2953", "is_paper": true, - "keywords": [], + "keywords": [ + "reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.288.pdf", "paper_type": "Main-Oral", @@ -46414,7 +49035,11 @@ ], "id": "P2957", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.50.pdf", "paper_type": "findings", @@ -46445,7 +49070,10 @@ ], "id": "P2961", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.668.pdf", "paper_type": "Main-Oral", @@ -46481,7 +49109,11 @@ ], "id": "P2962", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.754.pdf", "paper_type": "Main-Poster", @@ -46519,7 +49151,10 @@ ], "id": "P2970", "is_paper": true, - "keywords": [], + "keywords": [ + "semantic parsing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.787.pdf", "paper_type": "Main-Poster", @@ -46554,7 +49189,11 @@ ], "id": "P2971", "is_paper": true, - "keywords": [], + "keywords": [ + "speech and vision", + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.848.pdf", "paper_type": "Main-Oral", @@ -46589,7 +49228,10 @@ ], "id": "P2985", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.94.pdf", "paper_type": "Main-Poster", @@ -46623,7 +49265,10 @@ ], "id": "P2987", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodal summarization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.764.pdf", "paper_type": "findings", @@ -46657,7 +49302,10 @@ ], "id": "P299", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge graphs" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.71.pdf", "paper_type": "Main-Poster", @@ -46693,7 +49341,10 @@ ], "id": "P2991", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic creation and evaluation of language resources" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.626.pdf", "paper_type": "Main-Poster", @@ -46728,7 +49379,10 @@ ], "id": "P2992", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodal summarization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.165.pdf", "paper_type": "Main-Poster", @@ -46761,7 +49415,12 @@ ], "id": "P2993", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-task approaches (large definition)" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.570.pdf", "paper_type": "Main-Poster", @@ -46796,7 +49455,12 @@ ], "id": "P300", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.211.pdf", "paper_type": "findings", @@ -46831,7 +49495,12 @@ ], "id": "P3005", "is_paper": true, - "keywords": [], + "keywords": [ + "speech and vision" + ], + "languages": [ + "mandarin" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.884.pdf", "paper_type": "findings", @@ -46864,7 +49533,10 @@ ], "id": "P3011", "is_paper": true, - "keywords": [], + "keywords": [ + "rhetoric and framing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.589.pdf", "paper_type": "findings", @@ -46899,7 +49571,10 @@ ], "id": "P3014", "is_paper": true, - "keywords": [], + "keywords": [ + "generalization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.309.pdf", "paper_type": "Main-Oral", @@ -46934,7 +49609,10 @@ ], "id": "P3017", "is_paper": true, - "keywords": [], + "keywords": [ + "speech translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.447.pdf", "paper_type": "findings", @@ -46970,7 +49648,12 @@ ], "id": "P302", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation", + "methodology", + "negative results" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.674.pdf", "paper_type": "Main-Oral", @@ -47005,7 +49688,10 @@ ], "id": "P3022", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.526.pdf", "paper_type": "Main-Oral", @@ -47040,7 +49726,10 @@ ], "id": "P3024", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.11.pdf", "paper_type": "Main-Poster", @@ -47074,7 +49763,11 @@ ], "id": "P3032", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.766.pdf", "paper_type": "Main-Poster", @@ -47110,7 +49803,10 @@ ], "id": "P3035", "is_paper": true, - "keywords": [], + "keywords": [ + "hierarchical & concept explanations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.317.pdf", "paper_type": "findings", @@ -47144,7 +49840,10 @@ ], "id": "P304", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot/zero-shot mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.564.pdf", "paper_type": "findings", @@ -47180,7 +49879,10 @@ ], "id": "P3040", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.287.pdf", "paper_type": "Main-Poster", @@ -47214,7 +49916,10 @@ ], "id": "P3042", "is_paper": true, - "keywords": [], + "keywords": [ + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.280.pdf", "paper_type": "Main-Poster", @@ -47247,7 +49952,10 @@ ], "id": "P3043", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.751.pdf", "paper_type": "Main-Poster", @@ -47280,7 +49988,11 @@ ], "id": "P3047", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer", + "mutlilingual representations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.189.pdf", "paper_type": "findings", @@ -47313,7 +50025,11 @@ ], "id": "P3052", "is_paper": true, - "keywords": [], + "keywords": [ + "retrieval-augmented models", + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.45.pdf", "paper_type": "Main-Oral", @@ -47343,7 +50059,10 @@ ], "id": "P3057", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.728.pdf", "paper_type": "findings", @@ -47382,7 +50101,11 @@ ], "id": "P3058", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge augmented", + "grounded dialog" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.364.pdf", "paper_type": "Main-Poster", @@ -47416,7 +50139,12 @@ ], "id": "P3059", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation methodologies", + "evaluation", + "statistical testing for evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.404.pdf", "paper_type": "findings", @@ -47452,7 +50180,10 @@ ], "id": "P3061", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.696.pdf", "paper_type": "Main-Poster", @@ -47486,7 +50217,10 @@ ], "id": "P3065", "is_paper": true, - "keywords": [], + "keywords": [ + "image text matching" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.298.pdf", "paper_type": "Main-Poster", @@ -47518,7 +50252,10 @@ ], "id": "P3066", "is_paper": true, - "keywords": [], + "keywords": [ + "re-ranking" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.193.pdf", "paper_type": "findings", @@ -47550,7 +50287,10 @@ ], "id": "P3069", "is_paper": true, - "keywords": [], + "keywords": [ + "speech translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.216.pdf", "paper_type": "Main-Poster", @@ -47585,7 +50325,10 @@ ], "id": "P3072", "is_paper": true, - "keywords": [], + "keywords": [ + "generalization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.79.pdf", "paper_type": "findings", @@ -47618,7 +50361,10 @@ ], "id": "P3074", "is_paper": true, - "keywords": [], + "keywords": [ + "linguistic theories" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.489.pdf", "paper_type": "Main-Poster", @@ -47651,7 +50397,10 @@ ], "id": "P3081", "is_paper": true, - "keywords": [], + "keywords": [ + "representation learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.506.pdf", "paper_type": "Main-Poster", @@ -47689,7 +50438,10 @@ ], "id": "P3082", "is_paper": true, - "keywords": [], + "keywords": [ + "analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.93.pdf", "paper_type": "findings", @@ -47722,7 +50474,10 @@ ], "id": "P3085", "is_paper": true, - "keywords": [], + "keywords": [ + "reproducibility" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.157.pdf", "paper_type": "Main-Poster", @@ -47754,7 +50509,12 @@ ], "id": "P3087", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot generation", + "text-to-text generation", + "interactive and collaborative generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.51.pdf", "paper_type": "Main-Poster", @@ -47785,7 +50545,10 @@ ], "id": "P3091", "is_paper": true, - "keywords": [], + "keywords": [ + "dependency parsing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.459.pdf", "paper_type": "findings", @@ -47819,7 +50582,10 @@ ], "id": "P3093", "is_paper": true, - "keywords": [], + "keywords": [ + "model architectures" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.253.pdf", "paper_type": "Main-Poster", @@ -47850,7 +50616,11 @@ ], "id": "P3101", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation", + "biases" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.122.pdf", "paper_type": "Main-Oral", @@ -47886,7 +50656,10 @@ ], "id": "P3103", "is_paper": true, - "keywords": [], + "keywords": [ + "bias/toxicity" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.646.pdf", "paper_type": "Main-Poster", @@ -47920,7 +50693,12 @@ ], "id": "P3107", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets", + "evaluation", + "metrics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.641.pdf", "paper_type": "findings", @@ -47953,7 +50731,10 @@ ], "id": "P3109", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal pretraining" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.464.pdf", "paper_type": "Main-Poster", @@ -47986,7 +50767,10 @@ ], "id": "P3114", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge graphs" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.458.pdf", "paper_type": "findings", @@ -48020,7 +50804,10 @@ ], "id": "P3116", "is_paper": true, - "keywords": [], + "keywords": [ + "legal nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.865.pdf", "paper_type": "Main-Poster", @@ -48055,7 +50842,10 @@ ], "id": "P3117", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.439.pdf", "paper_type": "Main-Poster", @@ -48088,7 +50878,10 @@ ], "id": "P312", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.666.pdf", "paper_type": "Main-Poster", @@ -48121,7 +50914,10 @@ ], "id": "P3121", "is_paper": true, - "keywords": [], + "keywords": [ + "mutlilingual representations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.350.pdf", "paper_type": "findings", @@ -48158,7 +50954,10 @@ ], "id": "P3126", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual / low resource" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.684.pdf", "paper_type": "Main-Poster", @@ -48192,7 +50991,12 @@ ], "id": "P3129", "is_paper": true, - "keywords": [], + "keywords": [ + "graph-based methods", + "representation learning", + "graphical models" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.263.pdf", "paper_type": "Main-Poster", @@ -48228,7 +51032,10 @@ ], "id": "P3135", "is_paper": true, - "keywords": [], + "keywords": [ + "efficient mt training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.448.pdf", "paper_type": "Main-Poster", @@ -48263,7 +51070,10 @@ ], "id": "P3138", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.648.pdf", "paper_type": "Main-Poster", @@ -48297,7 +51107,10 @@ ], "id": "P3145", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.290.pdf", "paper_type": "Main-Oral", @@ -48330,7 +51143,11 @@ ], "id": "P3147", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.754.pdf", "paper_type": "findings", @@ -48364,7 +51181,10 @@ ], "id": "P3150", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/unfairness mitigation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.386.pdf", "paper_type": "findings", @@ -48396,7 +51216,15 @@ ], "id": "P3152", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot/zero-shot mt", + "modelling" + ], + "languages": [ + "german", + "polish", + "french" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.521.pdf", "paper_type": "findings", @@ -48434,7 +51262,11 @@ ], "id": "P3153", "is_paper": true, - "keywords": [], + "keywords": [ + "passage retrieval", + "document representation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.801.pdf", "paper_type": "findings", @@ -48467,7 +51299,10 @@ ], "id": "P3155", "is_paper": true, - "keywords": [], + "keywords": [ + "human factors in nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.88.pdf", "paper_type": "Main-Poster", @@ -48503,7 +51338,11 @@ ], "id": "P3165", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented", + "multilingual / low resource" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.230.pdf", "paper_type": "findings", @@ -48536,7 +51375,10 @@ ], "id": "P3167", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.237.pdf", "paper_type": "Main-Poster", @@ -48569,7 +51411,10 @@ ], "id": "P3174", "is_paper": true, - "keywords": [], + "keywords": [ + "speech translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.153.pdf", "paper_type": "Main-Poster", @@ -48601,7 +51446,14 @@ ], "id": "P3175", "is_paper": true, - "keywords": [], + "keywords": [ + "linguistic theories", + "computational psycholinguistics" + ], + "languages": [ + "spanish", + "galician" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.12.pdf", "paper_type": "Main-Oral", @@ -48633,7 +51485,13 @@ ], "id": "P3179", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "language resources", + "nlp datasets", + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.188.pdf", "paper_type": "Main-Poster", @@ -48668,7 +51526,12 @@ ], "id": "P3180", "is_paper": true, - "keywords": [], + "keywords": [ + "lessons from deployment", + "evaluation", + "forgotten lessons" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.187.pdf", "paper_type": "Main-Poster", @@ -48701,7 +51564,10 @@ ], "id": "P3183", "is_paper": true, - "keywords": [], + "keywords": [ + "retrieval-augmented models" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.136.pdf", "paper_type": "Main-Oral", @@ -48732,7 +51598,15 @@ ], "id": "P3184", "is_paper": true, - "keywords": [], + "keywords": [ + "language resources" + ], + "languages": [ + "french", + "german", + "italian", + "spanish" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.240.pdf", "paper_type": "findings", @@ -48766,7 +51640,11 @@ ], "id": "P3188", "is_paper": true, - "keywords": [], + "keywords": [ + "lessons from deployment", + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.763.pdf", "paper_type": "findings", @@ -48799,7 +51677,12 @@ ], "id": "P319", "is_paper": true, - "keywords": [], + "keywords": [ + "linguistic theories" + ], + "languages": [ + "german" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.122.pdf", "paper_type": "Main-Poster", @@ -48834,7 +51717,11 @@ ], "id": "P3190", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation and metrics", + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.277.pdf", "paper_type": "findings", @@ -48866,7 +51753,11 @@ ], "id": "P3191", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction", + "document-level extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.563.pdf", "paper_type": "Main-Poster", @@ -48901,7 +51792,10 @@ ], "id": "P3197", "is_paper": true, - "keywords": [], + "keywords": [ + "reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.733.pdf", "paper_type": "Main-Oral", @@ -48932,7 +51826,10 @@ ], "id": "P3198", "is_paper": true, - "keywords": [], + "keywords": [ + "data-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.619.pdf", "paper_type": "Main-Poster", @@ -48966,7 +51863,17 @@ ], "id": "P3203", "is_paper": true, - "keywords": [], + "keywords": [ + "(non-)generalizability", + "evaluation" + ], + "languages": [ + "german", + "italian", + "french", + "farsi", + "danish" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.23.pdf", "paper_type": "Main-Poster", @@ -49002,7 +51909,38 @@ ], "id": "P3205", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "benchmarking", + "language resources", + "multilingual corpora", + "datasets for low resource languages" + ], + "languages": [ + "assamese", + "bodo", + "bengali", + "dogri", + "konkani", + "gujarati", + "hindi", + "khasi", + "kannada", + "kashmiri", + "maithili", + "malayalam", + "manipuri", + "marathi", + "nepali", + "odia", + "punjabi", + "sanskrit", + "santhali", + "sindhi", + "tamil", + "telugu", + "urdu" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.693.pdf", "paper_type": "Main-Poster", @@ -49037,7 +51975,10 @@ ], "id": "P3209", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation methodologies" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.324.pdf", "paper_type": "Main-Oral", @@ -49071,7 +52012,10 @@ ], "id": "P3216", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.444.pdf", "paper_type": "Main-Oral", @@ -49103,7 +52047,17 @@ ], "id": "P322", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "benchmarking", + "language resources", + "automatic creation and evaluation of language resources", + "nlp datasets", + "datasets for low resource languages" + ], + "languages": [ + "arabic" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.901.pdf", "paper_type": "Main-Poster", @@ -49141,7 +52095,10 @@ ], "id": "P3225", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.586.pdf", "paper_type": "findings", @@ -49175,7 +52132,11 @@ ], "id": "P3227", "is_paper": true, - "keywords": [], + "keywords": [ + "(non-)generalizability", + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.796.pdf", "paper_type": "Main-Oral", @@ -49210,7 +52171,10 @@ ], "id": "P3228", "is_paper": true, - "keywords": [], + "keywords": [ + "model architectures" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.849.pdf", "paper_type": "Main-Poster", @@ -49245,7 +52209,11 @@ ], "id": "P3229", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.107.pdf", "paper_type": "Main-Poster", @@ -49277,7 +52245,11 @@ ], "id": "P3234", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation", + "multi-document summarization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.686.pdf", "paper_type": "findings", @@ -49312,7 +52284,11 @@ ], "id": "P324", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual / low resource", + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.70.pdf", "paper_type": "Main-Poster", @@ -49346,7 +52322,10 @@ ], "id": "P3241", "is_paper": true, - "keywords": [], + "keywords": [ + "domain adaptation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.888.pdf", "paper_type": "Main-Poster", @@ -49385,7 +52364,10 @@ ], "id": "P3242", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.380.pdf", "paper_type": "findings", @@ -49424,7 +52406,10 @@ ], "id": "P3243", "is_paper": true, - "keywords": [], + "keywords": [ + "entity linking/disambiguation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.542.pdf", "paper_type": "Main-Poster", @@ -49460,6 +52445,7 @@ "id": "P3245", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.413.pdf", "paper_type": "Main-Oral", @@ -49493,7 +52479,10 @@ ], "id": "P3246", "is_paper": true, - "keywords": [], + "keywords": [ + "speech technologies" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.132.pdf", "paper_type": "Main-Poster", @@ -49525,7 +52514,10 @@ ], "id": "P3250", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.62.pdf", "paper_type": "Main-Poster", @@ -49558,7 +52550,10 @@ ], "id": "P3259", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.346.pdf", "paper_type": "Main-Oral", @@ -49589,7 +52584,10 @@ ], "id": "P326", "is_paper": true, - "keywords": [], + "keywords": [ + "reading comprehension" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.55.pdf", "paper_type": "Main-Oral", @@ -49625,7 +52623,10 @@ ], "id": "P3267", "is_paper": true, - "keywords": [], + "keywords": [ + "model compression methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.249.pdf", "paper_type": "Main-Oral", @@ -49657,7 +52658,13 @@ ], "id": "P327", "is_paper": true, - "keywords": [], + "keywords": [ + "speech translation" + ], + "languages": [ + "german", + "spanish" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.745.pdf", "paper_type": "Main-Poster", @@ -49690,6 +52697,15 @@ "id": "P3273", "is_paper": true, "keywords": [], + "languages": [ + "spanish", + "french", + "german", + "italian", + "dutch", + "portuguese", + "romanian" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.67.pdf", "paper_type": "Main-Poster", @@ -49722,7 +52738,10 @@ ], "id": "P3275", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.134.pdf", "paper_type": "Main-Poster", @@ -49761,7 +52780,12 @@ ], "id": "P328", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation", + "long-form summarization", + "factuality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.587.pdf", "paper_type": "Main-Oral", @@ -49793,7 +52817,11 @@ ], "id": "P3281", "is_paper": true, - "keywords": [], + "keywords": [ + "generalization", + "parameter-efficient finetuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.288.pdf", "paper_type": "findings", @@ -49826,7 +52854,10 @@ ], "id": "P3287", "is_paper": true, - "keywords": [], + "keywords": [ + "human-in-the-loop / active learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.144.pdf", "paper_type": "findings", @@ -49860,7 +52891,15 @@ ], "id": "P3291", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic speech recognition" + ], + "languages": [ + "gronings", + "west-frisian", + "besemah", + "nasal" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.42.pdf", "paper_type": "Main-Oral", @@ -49893,7 +52932,11 @@ ], "id": "P3294", "is_paper": true, - "keywords": [], + "keywords": [ + "robustness", + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.74.pdf", "paper_type": "Main-Poster", @@ -49925,7 +52968,10 @@ ], "id": "P3302", "is_paper": true, - "keywords": [], + "keywords": [ + "commonsense qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.53.pdf", "paper_type": "Main-Poster", @@ -49964,7 +53010,10 @@ ], "id": "P3304", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.254.pdf", "paper_type": "Main-Poster", @@ -49997,7 +53046,10 @@ ], "id": "P3308", "is_paper": true, - "keywords": [], + "keywords": [ + "coherence" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.663.pdf", "paper_type": "findings", @@ -50033,7 +53085,14 @@ ], "id": "P3309", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "scaling", + "ethics", + "security and privacy", + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.805.pdf", "paper_type": "Main-Poster", @@ -50068,7 +53127,10 @@ ], "id": "P331", "is_paper": true, - "keywords": [], + "keywords": [ + "optimization methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.356.pdf", "paper_type": "findings", @@ -50102,7 +53164,12 @@ ], "id": "P3313", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation" + ], + "languages": [ + "german" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.74.pdf", "paper_type": "findings", @@ -50138,7 +53205,12 @@ ], "id": "P3322", "is_paper": true, - "keywords": [], + "keywords": [ + "transparency", + "policy and governance", + "reflections and critiques" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.734.pdf", "paper_type": "Main-Oral", @@ -50170,7 +53242,10 @@ ], "id": "P3324", "is_paper": true, - "keywords": [], + "keywords": [ + "parameter-efficient finetuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.418.pdf", "paper_type": "Main-Poster", @@ -50206,7 +53281,10 @@ ], "id": "P3325", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.788.pdf", "paper_type": "Main-Poster", @@ -50241,7 +53319,10 @@ ], "id": "P3326", "is_paper": true, - "keywords": [], + "keywords": [ + "fact checking, rumour/misinformation detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.289.pdf", "paper_type": "findings", @@ -50273,7 +53354,10 @@ ], "id": "P3327", "is_paper": true, - "keywords": [], + "keywords": [ + "stance detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.752.pdf", "paper_type": "Main-Oral", @@ -50310,7 +53394,10 @@ ], "id": "P3329", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual pre-training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.388.pdf", "paper_type": "Main-Poster", @@ -50342,7 +53429,10 @@ ], "id": "P3332", "is_paper": true, - "keywords": [], + "keywords": [ + "free-text/natural language explanations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.372.pdf", "paper_type": "Main-Oral", @@ -50377,7 +53467,10 @@ ], "id": "P3334", "is_paper": true, - "keywords": [], + "keywords": [ + "hate-speech detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.676.pdf", "paper_type": "Main-Poster", @@ -50411,7 +53504,12 @@ ], "id": "P3340", "is_paper": true, - "keywords": [], + "keywords": [ + "data augmentation" + ], + "languages": [ + "basque" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.235.pdf", "paper_type": "findings", @@ -50444,7 +53542,10 @@ ], "id": "P3342", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.686.pdf", "paper_type": "Main-Poster", @@ -50477,7 +53578,10 @@ ], "id": "P3353", "is_paper": true, - "keywords": [], + "keywords": [ + "table qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.348.pdf", "paper_type": "Main-Poster", @@ -50512,7 +53616,15 @@ ], "id": "P3356", "is_paper": true, - "keywords": [], + "keywords": [ + "scaling" + ], + "languages": [ + "basque", + "spanish", + "swahili", + "finnish" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.492.pdf", "paper_type": "findings", @@ -50544,7 +53656,38 @@ ], "id": "P3357", "is_paper": true, - "keywords": [], + "keywords": [ + "benchmarking", + "multilingual corpora", + "nlp datasets", + "datasets for low resource languages" + ], + "languages": [ + "assamese", + "bodo", + "bengali", + "dogri", + "konkani", + "gujarati", + "hindi", + "khasi", + "kannada", + "kashmiri", + "maithili", + "malayalam", + "manipuri", + "marathi", + "nepali", + "odia", + "punjabi", + "sanskrit", + "santhali", + "sindhi", + "tamil", + "telugu", + "urdu", + "indian" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.71.pdf", "paper_type": "Main-Poster", @@ -50575,7 +53718,10 @@ ], "id": "P3367", "is_paper": true, - "keywords": [], + "keywords": [ + "extractive summarisation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.29.pdf", "paper_type": "Main-Poster", @@ -50611,7 +53757,15 @@ ], "id": "P3371", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "language resources", + "nlp datasets", + "datasets for low resource languages" + ], + "languages": [ + "hebrew" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.460.pdf", "paper_type": "findings", @@ -50646,7 +53800,10 @@ ], "id": "P3374", "is_paper": true, - "keywords": [], + "keywords": [ + "generalization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.72.pdf", "paper_type": "Main-Oral", @@ -50679,7 +53836,10 @@ ], "id": "P3377", "is_paper": true, - "keywords": [], + "keywords": [ + "analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.69.pdf", "paper_type": "Main-Poster", @@ -50711,7 +53871,10 @@ ], "id": "P3380", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.813.pdf", "paper_type": "Main-Poster", @@ -50745,7 +53908,14 @@ ], "id": "P3382", "is_paper": true, - "keywords": [], + "keywords": [ + "morphological inflection", + "morphological analysis", + "phonology" + ], + "languages": [ + "arabic" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.101.pdf", "paper_type": "Main-Poster", @@ -50778,7 +53948,10 @@ ], "id": "P3388", "is_paper": true, - "keywords": [], + "keywords": [ + "code generation and understanding" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.799.pdf", "paper_type": "Main-Poster", @@ -50810,7 +53983,10 @@ ], "id": "P3389", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.769.pdf", "paper_type": "Main-Poster", @@ -50844,7 +54020,10 @@ ], "id": "P339", "is_paper": true, - "keywords": [], + "keywords": [ + "fact checking, rumour/misinformation detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.815.pdf", "paper_type": "Main-Poster", @@ -50875,7 +54054,10 @@ ], "id": "P3390", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/unfairness mitigation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.876.pdf", "paper_type": "Main-Poster", @@ -50909,7 +54091,11 @@ ], "id": "P3391", "is_paper": true, - "keywords": [], + "keywords": [ + "speech and vision", + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.649.pdf", "paper_type": "Main-Oral", @@ -50945,7 +54131,10 @@ ], "id": "P3397", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodal qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.311.pdf", "paper_type": "findings", @@ -50977,7 +54166,10 @@ ], "id": "P34", "is_paper": true, - "keywords": [], + "keywords": [ + "modelling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.897.pdf", "paper_type": "findings", @@ -51010,7 +54202,10 @@ ], "id": "P3403", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.508.pdf", "paper_type": "Main-Poster", @@ -51047,7 +54242,10 @@ ], "id": "P341", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.518.pdf", "paper_type": "Main-Poster", @@ -51080,7 +54278,10 @@ ], "id": "P3410", "is_paper": true, - "keywords": [], + "keywords": [ + "probing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.534.pdf", "paper_type": "Main-Poster", @@ -51114,7 +54315,10 @@ ], "id": "P3412", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.590.pdf", "paper_type": "findings", @@ -51148,7 +54352,12 @@ ], "id": "P3413", "is_paper": true, - "keywords": [], + "keywords": [ + "morphological inflection" + ], + "languages": [ + "arabic" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.69.pdf", "paper_type": "Main-Poster", @@ -51181,7 +54390,11 @@ ], "id": "P3418", "is_paper": true, - "keywords": [], + "keywords": [ + "commonsense qa", + "reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.785.pdf", "paper_type": "Main-Poster", @@ -51226,7 +54439,10 @@ ], "id": "P3421", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal content generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.73.pdf", "paper_type": "Main-Oral", @@ -51258,7 +54474,10 @@ ], "id": "P3422", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.81.pdf", "paper_type": "Main-Poster", @@ -51293,7 +54512,10 @@ ], "id": "P3426", "is_paper": true, - "keywords": [], + "keywords": [ + "security and privacy" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.410.pdf", "paper_type": "Main-Poster", @@ -51329,7 +54551,10 @@ ], "id": "P3430", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.445.pdf", "paper_type": "findings", @@ -51361,7 +54586,10 @@ ], "id": "P3438", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic creation and evaluation of language resources" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.34.pdf", "paper_type": "Main-Oral", @@ -51393,7 +54621,10 @@ ], "id": "P3441", "is_paper": true, - "keywords": [], + "keywords": [ + "hate-speech detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.761.pdf", "paper_type": "Main-Poster", @@ -51424,7 +54655,10 @@ ], "id": "P3446", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.892.pdf", "paper_type": "Main-Poster", @@ -51457,7 +54691,10 @@ ], "id": "P3447", "is_paper": true, - "keywords": [], + "keywords": [ + "document-level extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.534.pdf", "paper_type": "findings", @@ -51491,7 +54728,10 @@ ], "id": "P3448", "is_paper": true, - "keywords": [], + "keywords": [ + "coreference resolution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.851.pdf", "paper_type": "Main-Oral", @@ -51525,7 +54765,10 @@ ], "id": "P345", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal pretraining" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.552.pdf", "paper_type": "findings", @@ -51556,7 +54799,10 @@ ], "id": "P3454", "is_paper": true, - "keywords": [], + "keywords": [ + "constituency parsing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.285.pdf", "paper_type": "Main-Oral", @@ -51587,7 +54833,10 @@ ], "id": "P3457", "is_paper": true, - "keywords": [], + "keywords": [ + "representation learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.475.pdf", "paper_type": "findings", @@ -51623,7 +54872,10 @@ ], "id": "P3458", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.539.pdf", "paper_type": "Main-Poster", @@ -51657,7 +54909,10 @@ ], "id": "P3463", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.55.pdf", "paper_type": "Main-Poster", @@ -51688,7 +54943,10 @@ ], "id": "P3472", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.103.pdf", "paper_type": "Main-Poster", @@ -51720,7 +54978,10 @@ ], "id": "P3473", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.17.pdf", "paper_type": "findings", @@ -51757,7 +55018,10 @@ ], "id": "P3475", "is_paper": true, - "keywords": [], + "keywords": [ + "security and privacy" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.129.pdf", "paper_type": "Main-Poster", @@ -51794,7 +55058,10 @@ ], "id": "P348", "is_paper": true, - "keywords": [], + "keywords": [ + "model compression methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.692.pdf", "paper_type": "findings", @@ -51841,7 +55108,10 @@ ], "id": "P3482", "is_paper": true, - "keywords": [], + "keywords": [ + "semantic parsing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.86.pdf", "paper_type": "findings", @@ -51873,7 +55143,10 @@ ], "id": "P3486", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.683.pdf", "paper_type": "findings", @@ -51907,7 +55180,12 @@ ], "id": "P3487", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.593.pdf", "paper_type": "Main-Poster", @@ -51942,7 +55220,11 @@ ], "id": "P3490", "is_paper": true, - "keywords": [], + "keywords": [ + "benchmarking", + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.895.pdf", "paper_type": "Main-Poster", @@ -51978,7 +55260,10 @@ ], "id": "P3493", "is_paper": true, - "keywords": [], + "keywords": [ + "language/cultural bias analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.170.pdf", "paper_type": "findings", @@ -52015,7 +55300,13 @@ ], "id": "P3495", "is_paper": true, - "keywords": [], + "keywords": [ + "benchmarking", + "nlp datasets", + "evaluation", + "metrics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.716.pdf", "paper_type": "Main-Poster", @@ -52048,7 +55339,11 @@ ], "id": "P3497", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction", + "document-level extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.604.pdf", "paper_type": "Main-Poster", @@ -52082,7 +55377,13 @@ ], "id": "P35", "is_paper": true, - "keywords": [], + "keywords": [ + "benchmarking", + "nlp datasets", + "evaluation methodologies", + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.817.pdf", "paper_type": "Main-Poster", @@ -52114,7 +55415,10 @@ ], "id": "P3504", "is_paper": true, - "keywords": [], + "keywords": [ + "semantic parsing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.810.pdf", "paper_type": "Main-Poster", @@ -52149,7 +55453,10 @@ ], "id": "P3506", "is_paper": true, - "keywords": [], + "keywords": [ + "semantic parsing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.177.pdf", "paper_type": "Main-Poster", @@ -52182,7 +55489,10 @@ ], "id": "P3512", "is_paper": true, - "keywords": [], + "keywords": [ + "feature attribution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.622.pdf", "paper_type": "findings", @@ -52216,7 +55526,11 @@ ], "id": "P3518", "is_paper": true, - "keywords": [], + "keywords": [ + "table qa", + "open-domain qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.133.pdf", "paper_type": "Main-Poster", @@ -52254,7 +55568,10 @@ ], "id": "P352", "is_paper": true, - "keywords": [], + "keywords": [ + "feature attribution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.310.pdf", "paper_type": "Main-Poster", @@ -52294,7 +55611,12 @@ ], "id": "P3524", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "language resources", + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.774.pdf", "paper_type": "Main-Poster", @@ -52328,7 +55650,10 @@ ], "id": "P353", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.505.pdf", "paper_type": "findings", @@ -52365,7 +55690,10 @@ ], "id": "P3530", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-modal dialogue systems" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.345.pdf", "paper_type": "Main-Poster", @@ -52401,7 +55729,14 @@ ], "id": "P3533", "is_paper": true, - "keywords": [], + "keywords": [ + "mixed language" + ], + "languages": [ + "indonesian", + "malay", + "singlish" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.711.pdf", "paper_type": "Main-Oral", @@ -52435,7 +55770,10 @@ ], "id": "P3537", "is_paper": true, - "keywords": [], + "keywords": [ + "table qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.551.pdf", "paper_type": "Main-Poster", @@ -52466,7 +55804,10 @@ ], "id": "P3539", "is_paper": true, - "keywords": [], + "keywords": [ + "mathematical nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.106.pdf", "paper_type": "Main-Poster", @@ -52497,7 +55838,12 @@ ], "id": "P3540", "is_paper": true, - "keywords": [], + "keywords": [ + "logical reasoning", + "reasoning", + "math qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.651.pdf", "paper_type": "findings", @@ -52531,7 +55877,10 @@ ], "id": "P3541", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge base construction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.705.pdf", "paper_type": "Main-Poster", @@ -52564,7 +55913,11 @@ ], "id": "P3542", "is_paper": true, - "keywords": [], + "keywords": [ + "graph-based methods", + "word embeddings" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.790.pdf", "paper_type": "Main-Oral", @@ -52598,7 +55951,10 @@ ], "id": "P3546", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.526.pdf", "paper_type": "findings", @@ -52631,7 +55987,11 @@ ], "id": "P3549", "is_paper": true, - "keywords": [], + "keywords": [ + "passage retrieval", + "dense retrieval" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.710.pdf", "paper_type": "Main-Poster", @@ -52666,7 +56026,10 @@ ], "id": "P355", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge augmented" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.97.pdf", "paper_type": "Main-Poster", @@ -52700,7 +56063,10 @@ ], "id": "P3557", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.687.pdf", "paper_type": "findings", @@ -52734,7 +56100,10 @@ ], "id": "P3563", "is_paper": true, - "keywords": [], + "keywords": [ + "probing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.173.pdf", "paper_type": "Main-Poster", @@ -52772,7 +56141,11 @@ ], "id": "P3571", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented", + "dialogue state tracking" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.81.pdf", "paper_type": "Main-Poster", @@ -52804,7 +56177,11 @@ ], "id": "P3572", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot/zero-shot mt", + "scaling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.524.pdf", "paper_type": "Main-Oral", @@ -52837,7 +56214,11 @@ ], "id": "P3576", "is_paper": true, - "keywords": [], + "keywords": [ + "argument mining", + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.322.pdf", "paper_type": "Main-Oral", @@ -52868,7 +56249,10 @@ ], "id": "P3579", "is_paper": true, - "keywords": [], + "keywords": [ + "emotion detection and analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.824.pdf", "paper_type": "Main-Poster", @@ -52900,7 +56284,10 @@ ], "id": "P3593", "is_paper": true, - "keywords": [], + "keywords": [ + "table qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.53.pdf", "paper_type": "findings", @@ -52935,7 +56322,10 @@ ], "id": "P3596", "is_paper": true, - "keywords": [], + "keywords": [ + "code-switching" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.318.pdf", "paper_type": "findings", @@ -52977,7 +56367,15 @@ ], "id": "P3601", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "benchmarking", + "automatic creation and evaluation of language resources", + "nlp datasets" + ], + "languages": [ + "korean" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.370.pdf", "paper_type": "Main-Oral", @@ -53008,7 +56406,11 @@ ], "id": "P3607", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis", + "quantiative analyses of news and/or social media" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.132.pdf", "paper_type": "Main-Poster", @@ -53038,7 +56440,13 @@ ], "id": "P3608", "is_paper": true, - "keywords": [], + "keywords": [ + "(non-)generalizability", + "evaluation", + "ai hype & expectations", + "lessons from other fields" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.675.pdf", "paper_type": "Main-Poster", @@ -53070,7 +56478,10 @@ ], "id": "P3614", "is_paper": true, - "keywords": [], + "keywords": [ + "dependency parsing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.124.pdf", "paper_type": "Main-Oral", @@ -53104,7 +56515,10 @@ ], "id": "P3615", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.757.pdf", "paper_type": "findings", @@ -53138,7 +56552,10 @@ ], "id": "P3625", "is_paper": true, - "keywords": [], + "keywords": [ + "financial/business nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.679.pdf", "paper_type": "Main-Poster", @@ -53170,7 +56587,10 @@ ], "id": "P3626", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.50.pdf", "paper_type": "Main-Poster", @@ -53203,7 +56623,10 @@ ], "id": "P3627", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.58.pdf", "paper_type": "Main-Poster", @@ -53237,7 +56660,10 @@ ], "id": "P3629", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/unfairness mitigation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.8.pdf", "paper_type": "findings", @@ -53271,7 +56697,16 @@ ], "id": "P3630", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual corpora", + "nlp datasets" + ], + "languages": [ + "french", + "german", + "italian", + "spanish" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.54.pdf", "paper_type": "findings", @@ -53305,7 +56740,10 @@ ], "id": "P3633", "is_paper": true, - "keywords": [], + "keywords": [ + "phrase/sentence embedding" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.125.pdf", "paper_type": "findings", @@ -53337,7 +56775,11 @@ ], "id": "P3636", "is_paper": true, - "keywords": [], + "keywords": [ + "explanation faithfulness", + "feature attribution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.867.pdf", "paper_type": "findings", @@ -53370,7 +56812,10 @@ ], "id": "P3638", "is_paper": true, - "keywords": [], + "keywords": [ + "grounded dialog" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.561.pdf", "paper_type": "Main-Poster", @@ -53402,7 +56847,10 @@ ], "id": "P3642", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.408.pdf", "paper_type": "Main-Poster", @@ -53436,7 +56884,12 @@ ], "id": "P3649", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge augmented", + "applications", + "grounded dialog" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.423.pdf", "paper_type": "findings", @@ -53474,7 +56927,10 @@ ], "id": "P3655", "is_paper": true, - "keywords": [], + "keywords": [ + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.112.pdf", "paper_type": "findings", @@ -53507,7 +56963,10 @@ ], "id": "P3656", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.79.pdf", "paper_type": "Main-Poster", @@ -53540,7 +56999,10 @@ ], "id": "P3659", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal content generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.104.pdf", "paper_type": "findings", @@ -53572,7 +57034,10 @@ ], "id": "P3664", "is_paper": true, - "keywords": [], + "keywords": [ + "factuality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.400.pdf", "paper_type": "findings", @@ -53606,7 +57071,10 @@ ], "id": "P3667", "is_paper": true, - "keywords": [], + "keywords": [ + "mutlilingual representations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.439.pdf", "paper_type": "findings", @@ -53640,6 +57108,7 @@ "id": "P367", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.148.pdf", "paper_type": "Main-Oral", @@ -53671,7 +57140,14 @@ ], "id": "P3672", "is_paper": true, - "keywords": [], + "keywords": [ + "financial/business nlp" + ], + "languages": [ + "danish", + "french", + "german" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.662.pdf", "paper_type": "Main-Poster", @@ -53708,7 +57184,10 @@ ], "id": "P3680", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.380.pdf", "paper_type": "Main-Oral", @@ -53749,7 +57228,10 @@ ], "id": "P3684", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.279.pdf", "paper_type": "Main-Poster", @@ -53781,7 +57263,10 @@ ], "id": "P3686", "is_paper": true, - "keywords": [], + "keywords": [ + "transfer learning / domain adaptation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.31.pdf", "paper_type": "findings", @@ -53814,7 +57299,11 @@ ], "id": "P3687", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation", + "ethical considerations in nlp applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.878.pdf", "paper_type": "Main-Poster", @@ -53847,7 +57336,18 @@ ], "id": "P3693", "is_paper": true, - "keywords": [], + "keywords": [ + "(non-)generalizability", + "evaluation", + "methodology" + ], + "languages": [ + "arabic", + "german", + "spanish", + "swahili", + "turkish" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.335.pdf", "paper_type": "Main-Poster", @@ -53879,7 +57379,11 @@ ], "id": "P3698", "is_paper": true, - "keywords": [], + "keywords": [ + "math qa", + "table qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.834.pdf", "paper_type": "Main-Poster", @@ -53927,7 +57431,11 @@ ], "id": "P3699", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation", + "factuality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.344.pdf", "paper_type": "Main-Poster", @@ -53959,7 +57467,10 @@ ], "id": "P3701", "is_paper": true, - "keywords": [], + "keywords": [ + "phrase/sentence embedding" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.137.pdf", "paper_type": "Main-Poster", @@ -53992,7 +57503,10 @@ ], "id": "P3704", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.98.pdf", "paper_type": "Main-Poster", @@ -54024,7 +57538,11 @@ ], "id": "P3705", "is_paper": true, - "keywords": [], + "keywords": [ + "phrase/sentence embedding", + "word/phrase alignment" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.109.pdf", "paper_type": "findings", @@ -54064,7 +57582,12 @@ ], "id": "P3709", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation", + "reproducibility", + "statistical testing for evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.835.pdf", "paper_type": "Main-Poster", @@ -54100,7 +57623,10 @@ ], "id": "P3721", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.124.pdf", "paper_type": "Main-Poster", @@ -54135,7 +57661,11 @@ ], "id": "P3724", "is_paper": true, - "keywords": [], + "keywords": [ + "generalization", + "human-in-the-loop / active learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.636.pdf", "paper_type": "Main-Poster", @@ -54167,7 +57697,10 @@ ], "id": "P3729", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial attacks/examples/training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.430.pdf", "paper_type": "Main-Poster", @@ -54198,7 +57731,12 @@ ], "id": "P3730", "is_paper": true, - "keywords": [], + "keywords": [ + "right for the wrong reasons", + "(non-)generalizability", + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.459.pdf", "paper_type": "Main-Poster", @@ -54230,7 +57768,10 @@ ], "id": "P3734", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.491.pdf", "paper_type": "Main-Poster", @@ -54261,7 +57802,10 @@ ], "id": "P3736", "is_paper": true, - "keywords": [], + "keywords": [ + "parsing algorighms (symbolic, theoritical results)" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.6.pdf", "paper_type": "Main-Poster", @@ -54296,7 +57840,10 @@ ], "id": "P3738", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal matchine translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.722.pdf", "paper_type": "Main-Oral", @@ -54328,7 +57875,11 @@ ], "id": "P3750", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "document-level extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.607.pdf", "paper_type": "Main-Poster", @@ -54366,7 +57917,10 @@ ], "id": "P3759", "is_paper": true, - "keywords": [], + "keywords": [ + "security/privacy" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.74.pdf", "paper_type": "Main-Poster", @@ -54402,7 +57956,11 @@ ], "id": "P3760", "is_paper": true, - "keywords": [], + "keywords": [ + "probing", + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.333.pdf", "paper_type": "Main-Poster", @@ -54435,7 +57993,11 @@ ], "id": "P3763", "is_paper": true, - "keywords": [], + "keywords": [ + "conversation", + "communication" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.472.pdf", "paper_type": "Main-Poster", @@ -54468,7 +58030,10 @@ ], "id": "P3765", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.119.pdf", "paper_type": "findings", @@ -54506,7 +58071,11 @@ ], "id": "P3769", "is_paper": true, - "keywords": [], + "keywords": [ + "human behavior analysis", + "sociolinguistics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.8.pdf", "paper_type": "Main-Poster", @@ -54579,7 +58148,13 @@ ], "id": "P377", "is_paper": true, - "keywords": [], + "keywords": [ + "part-of-speech tagging", + "low-resources languages pos tagging, parsing and related tasks" + ], + "languages": [ + "african languages" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.609.pdf", "paper_type": "Main-Oral", @@ -54609,7 +58184,15 @@ ], "id": "P3773", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "language resources", + "nlp datasets", + "datasets for low resource languages" + ], + "languages": [ + "turkish" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.768.pdf", "paper_type": "Main-Poster", @@ -54644,7 +58227,10 @@ ], "id": "P3774", "is_paper": true, - "keywords": [], + "keywords": [ + "long-form summarization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.906.pdf", "paper_type": "Main-Poster", @@ -54678,7 +58264,12 @@ ], "id": "P378", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational summarization", + "evaluation", + "factuality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.779.pdf", "paper_type": "Main-Poster", @@ -54713,7 +58304,10 @@ ], "id": "P3780", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.436.pdf", "paper_type": "findings", @@ -54747,7 +58341,10 @@ ], "id": "P3782", "is_paper": true, - "keywords": [], + "keywords": [ + "lexical semantic change" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.135.pdf", "paper_type": "Main-Poster", @@ -54781,7 +58378,10 @@ ], "id": "P3784", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.36.pdf", "paper_type": "Main-Poster", @@ -54814,7 +58414,11 @@ ], "id": "P3786", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.783.pdf", "paper_type": "Main-Poster", @@ -54846,7 +58450,10 @@ ], "id": "P3788", "is_paper": true, - "keywords": [], + "keywords": [ + "embodied agents" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.787.pdf", "paper_type": "findings", @@ -54879,7 +58486,10 @@ ], "id": "P379", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.75.pdf", "paper_type": "Main-Poster", @@ -54911,7 +58521,11 @@ ], "id": "P3791", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-word expressions", + "metaphor" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.43.pdf", "paper_type": "Main-Oral", @@ -54944,7 +58558,10 @@ ], "id": "P3794", "is_paper": true, - "keywords": [], + "keywords": [ + "efficient models" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.441.pdf", "paper_type": "findings", @@ -54977,7 +58594,11 @@ ], "id": "P3796", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "scaling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.527.pdf", "paper_type": "findings", @@ -55010,7 +58631,12 @@ ], "id": "P3799", "is_paper": true, - "keywords": [], + "keywords": [ + "datasets for low resource languages" + ], + "languages": [ + "bemba" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.115.pdf", "paper_type": "Main-Poster", @@ -55048,7 +58674,10 @@ ], "id": "P3802", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.328.pdf", "paper_type": "Main-Poster", @@ -55081,7 +58710,10 @@ ], "id": "P3809", "is_paper": true, - "keywords": [], + "keywords": [ + "modelling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.443.pdf", "paper_type": "Main-Poster", @@ -55120,7 +58752,12 @@ ], "id": "P3810", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge graphs" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.450.pdf", "paper_type": "Main-Poster", @@ -55152,7 +58789,10 @@ ], "id": "P382", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.334.pdf", "paper_type": "findings", @@ -55191,7 +58831,17 @@ ], "id": "P3824", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "benchmarking", + "language resources", + "multilingual corpora", + "nlp datasets", + "datasets for low resource languages" + ], + "languages": [ + "hausa" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.646.pdf", "paper_type": "findings", @@ -55221,7 +58871,10 @@ ], "id": "P3828", "is_paper": true, - "keywords": [], + "keywords": [ + "lexical semantic change" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.52.pdf", "paper_type": "Main-Poster", @@ -55258,7 +58911,46 @@ ], "id": "P3832", "is_paper": true, - "keywords": [], + "keywords": [ + "less-resourced languages", + "resources for less-resourced languages" + ], + "languages": [ + "acehnese", + "moroccan arabic", + "egyptian arabic", + "bambara", + "balinese", + "bhojpuri", + "banjar", + "buginese", + "crimean tatar", + "southwestern dinka", + "dzongkha", + "friulian", + "nigerian fulfulde", + "guarani", + "chhattisgarhi", + "kashmiri", + "central kanuri", + "ligurian", + "limburgish", + "lombard", + "latgalian", + "magahi", + "meitei", + "maori", + "nuer", + "dari", + "southern pashto", + "sicilian", + "shan", + "sardinian", + "silesian", + "tamasheq", + "central atlas tamazight", + "venetian" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.154.pdf", "paper_type": "Main-Oral", @@ -55298,7 +58990,10 @@ ], "id": "P3833", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.228.pdf", "paper_type": "Main-Oral", @@ -55333,7 +59028,10 @@ ], "id": "P3836", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.594.pdf", "paper_type": "findings", @@ -55365,7 +59063,10 @@ ], "id": "P384", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.367.pdf", "paper_type": "Main-Poster", @@ -55398,7 +59099,12 @@ ], "id": "P3841", "is_paper": true, - "keywords": [], + "keywords": [ + "switch-code translation" + ], + "languages": [ + "vietnamese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.893.pdf", "paper_type": "findings", @@ -55433,7 +59139,11 @@ ], "id": "P3843", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation", + "negative results" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.98.pdf", "paper_type": "Main-Oral", @@ -55467,7 +59177,10 @@ ], "id": "P3845", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.357.pdf", "paper_type": "findings", @@ -55500,7 +59213,11 @@ ], "id": "P3847", "is_paper": true, - "keywords": [], + "keywords": [ + "textual entailment", + "natural language inference" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.772.pdf", "paper_type": "Main-Poster", @@ -55532,7 +59249,12 @@ ], "id": "P3849", "is_paper": true, - "keywords": [], + "keywords": [ + "(non-)reproducibility", + "evaluation", + "methodology" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.511.pdf", "paper_type": "Main-Poster", @@ -55567,7 +59289,10 @@ ], "id": "P3852", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.152.pdf", "paper_type": "Main-Poster", @@ -55600,7 +59325,10 @@ ], "id": "P3859", "is_paper": true, - "keywords": [], + "keywords": [ + "generalization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.317.pdf", "paper_type": "Main-Poster", @@ -55633,7 +59361,10 @@ ], "id": "P3860", "is_paper": true, - "keywords": [], + "keywords": [ + "topic modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.578.pdf", "paper_type": "Main-Poster", @@ -55672,7 +59403,10 @@ ], "id": "P3866", "is_paper": true, - "keywords": [], + "keywords": [ + "spoken language understanding" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.496.pdf", "paper_type": "Main-Poster", @@ -55707,7 +59441,11 @@ ], "id": "P3868", "is_paper": true, - "keywords": [], + "keywords": [ + "benchmarking", + "evaluation methodologies" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.113.pdf", "paper_type": "findings", @@ -55740,7 +59478,13 @@ ], "id": "P3876", "is_paper": true, - "keywords": [], + "keywords": [ + "polysemy", + "lexical semantic change", + "word embeddings", + "interpretability" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.176.pdf", "paper_type": "Main-Oral", @@ -55771,7 +59515,10 @@ ], "id": "P3878", "is_paper": true, - "keywords": [], + "keywords": [ + "topic modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.265.pdf", "paper_type": "findings", @@ -55808,7 +59555,10 @@ ], "id": "P3880", "is_paper": true, - "keywords": [], + "keywords": [ + "metrics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.428.pdf", "paper_type": "findings", @@ -55845,7 +59595,11 @@ ], "id": "P3883", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training", + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.724.pdf", "paper_type": "Main-Poster", @@ -55877,7 +59631,10 @@ ], "id": "P3888", "is_paper": true, - "keywords": [], + "keywords": [ + "human-subject application-grounded evaluations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.59.pdf", "paper_type": "Main-Poster", @@ -55912,7 +59669,13 @@ ], "id": "P3890", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation", + "human evaluation" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.435.pdf", "paper_type": "Main-Oral", @@ -55946,7 +59709,11 @@ ], "id": "P3903", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "multilingual extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.428.pdf", "paper_type": "Main-Poster", @@ -55980,7 +59747,10 @@ ], "id": "P3907", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial attacks/examples/training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.10.pdf", "paper_type": "findings", @@ -56012,7 +59782,11 @@ ], "id": "P3908", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.641.pdf", "paper_type": "Main-Poster", @@ -56047,7 +59821,10 @@ ], "id": "P3909", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.596.pdf", "paper_type": "Main-Poster", @@ -56081,7 +59858,10 @@ ], "id": "P391", "is_paper": true, - "keywords": [], + "keywords": [ + "cognitive modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.230.pdf", "paper_type": "Main-Poster", @@ -56112,7 +59892,10 @@ ], "id": "P3911", "is_paper": true, - "keywords": [], + "keywords": [ + "probing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.213.pdf", "paper_type": "Main-Poster", @@ -56144,7 +59927,10 @@ ], "id": "P3917", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.365.pdf", "paper_type": "findings", @@ -56176,7 +59962,11 @@ ], "id": "P392", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction", + "zero/few-shot extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.202.pdf", "paper_type": "Main-Poster", @@ -56209,7 +59999,10 @@ ], "id": "P3923", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.294.pdf", "paper_type": "findings", @@ -56244,7 +60037,12 @@ ], "id": "P3924", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [ + "icelandic" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.402.pdf", "paper_type": "Main-Oral", @@ -56278,7 +60076,13 @@ ], "id": "P3926", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation", + "methodology", + "ai hype & expectations", + "lessons from other fields" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.139.pdf", "paper_type": "findings", @@ -56312,7 +60116,10 @@ ], "id": "P3930", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual / low resource" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.468.pdf", "paper_type": "Main-Poster", @@ -56352,7 +60159,12 @@ ], "id": "P3938", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual pre-training" + ], + "languages": [ + "hundereds of low-resource languages" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.61.pdf", "paper_type": "Main-Oral", @@ -56388,7 +60200,11 @@ ], "id": "P3939", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial attacks/examples/training", + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.395.pdf", "paper_type": "Main-Poster", @@ -56421,7 +60237,10 @@ ], "id": "P394", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.61.pdf", "paper_type": "Main-Poster", @@ -56456,6 +60275,7 @@ "id": "P3940", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.425.pdf", "paper_type": "findings", @@ -56489,7 +60309,12 @@ ], "id": "P3944", "is_paper": true, - "keywords": [], + "keywords": [ + "historical nlp" + ], + "languages": [ + "greek" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.556.pdf", "paper_type": "Main-Poster", @@ -56524,7 +60349,11 @@ ], "id": "P3945", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation and metrics", + "task-oriented" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.116.pdf", "paper_type": "Main-Oral", @@ -56559,7 +60388,10 @@ ], "id": "P3948", "is_paper": true, - "keywords": [], + "keywords": [ + "image text matching" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.258.pdf", "paper_type": "findings", @@ -56598,7 +60430,10 @@ ], "id": "P395", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.738.pdf", "paper_type": "findings", @@ -56629,7 +60464,10 @@ ], "id": "P3951", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.36.pdf", "paper_type": "Main-Poster", @@ -56662,7 +60500,10 @@ ], "id": "P3953", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.886.pdf", "paper_type": "Main-Oral", @@ -56696,7 +60537,17 @@ ], "id": "P3954", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "language resources", + "multilingual corpora", + "nlp datasets", + "evaluation", + "datasets for low resource languages" + ], + "languages": [ + "croatian" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.142.pdf", "paper_type": "findings", @@ -56729,7 +60580,11 @@ ], "id": "P3958", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation", + "conversational summarization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.82.pdf", "paper_type": "Main-Poster", @@ -56763,7 +60618,11 @@ ], "id": "P3959", "is_paper": true, - "keywords": [], + "keywords": [ + "explanation faithfulness", + "feature attribution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.149.pdf", "paper_type": "Main-Poster", @@ -56796,7 +60655,11 @@ ], "id": "P3960", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation", + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.905.pdf", "paper_type": "Main-Poster", @@ -56828,7 +60691,12 @@ ], "id": "P3961", "is_paper": true, - "keywords": [], + "keywords": [ + "generalization", + "continual learning", + "meta learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.36.pdf", "paper_type": "findings", @@ -56863,7 +60731,10 @@ ], "id": "P3962", "is_paper": true, - "keywords": [], + "keywords": [ + "natural language inference" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.571.pdf", "paper_type": "Main-Poster", @@ -56900,7 +60771,10 @@ ], "id": "P3963", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.581.pdf", "paper_type": "Main-Poster", @@ -56935,7 +60809,10 @@ ], "id": "P3969", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.759.pdf", "paper_type": "Main-Poster", @@ -56966,7 +60843,10 @@ ], "id": "P3970", "is_paper": true, - "keywords": [], + "keywords": [ + "multilinguality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.623.pdf", "paper_type": "findings", @@ -56999,7 +60879,10 @@ ], "id": "P3973", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.512.pdf", "paper_type": "Main-Poster", @@ -57031,7 +60914,10 @@ ], "id": "P3976", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.868.pdf", "paper_type": "Main-Poster", @@ -57065,7 +60951,10 @@ ], "id": "P3977", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal pretraining" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.87.pdf", "paper_type": "Main-Poster", @@ -57097,7 +60986,11 @@ ], "id": "P3982", "is_paper": true, - "keywords": [], + "keywords": [ + "right for the wrong reasons", + "ai hype & expectations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.81.pdf", "paper_type": "findings", @@ -57129,7 +61022,11 @@ ], "id": "P3983", "is_paper": true, - "keywords": [], + "keywords": [ + "ethical considerations in nlp applications", + "reflections and critiques" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.502.pdf", "paper_type": "findings", @@ -57163,7 +61060,12 @@ ], "id": "P3985", "is_paper": true, - "keywords": [], + "keywords": [ + "semantic parsing", + "multi-task approaches (large definition)", + "low-resources languages pos tagging, parsing and related tasks" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.345.pdf", "paper_type": "findings", @@ -57197,7 +61099,11 @@ ], "id": "P399", "is_paper": true, - "keywords": [], + "keywords": [ + "hierarchical & concept explanations", + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.770.pdf", "paper_type": "findings", @@ -57228,7 +61134,25 @@ ], "id": "P3991", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingualism", + "linguistic variation", + "multilingual benchmarks", + "multilingual evaluation" + ], + "languages": [ + "french", + "spanish", + "italian", + "russian", + "slovak", + "arabic", + "finnish", + "swedish", + "swahili", + "mandarin chinese", + "german" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.396.pdf", "paper_type": "Main-Oral", @@ -57263,7 +61187,10 @@ ], "id": "P3992", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.119.pdf", "paper_type": "Main-Poster", @@ -57296,7 +61223,10 @@ ], "id": "P3993", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.795.pdf", "paper_type": "findings", @@ -57334,7 +61264,10 @@ ], "id": "P3998", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.60.pdf", "paper_type": "Main-Poster", @@ -57369,7 +61302,10 @@ ], "id": "P3999", "is_paper": true, - "keywords": [], + "keywords": [ + "free-text/natural language explanations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.25.pdf", "paper_type": "Main-Oral", @@ -57401,7 +61337,13 @@ ], "id": "P4005", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation and metrics", + "task-oriented", + "applications", + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.494.pdf", "paper_type": "Main-Poster", @@ -57442,7 +61384,10 @@ ], "id": "P4007", "is_paper": true, - "keywords": [], + "keywords": [ + "code generation and understanding" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.68.pdf", "paper_type": "Main-Poster", @@ -57472,7 +61417,10 @@ ], "id": "P4008", "is_paper": true, - "keywords": [], + "keywords": [ + "parsing algorighms (symbolic, theoritical results)" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.598.pdf", "paper_type": "Main-Poster", @@ -57504,7 +61452,10 @@ ], "id": "P4012", "is_paper": true, - "keywords": [], + "keywords": [ + "human-subject application-grounded evaluations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.264.pdf", "paper_type": "findings", @@ -57537,7 +61488,10 @@ ], "id": "P4013", "is_paper": true, - "keywords": [], + "keywords": [ + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.806.pdf", "paper_type": "Main-Poster", @@ -57568,7 +61522,10 @@ ], "id": "P4017", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.34.pdf", "paper_type": "Main-Poster", @@ -57604,7 +61561,14 @@ ], "id": "P4018", "is_paper": true, - "keywords": [], + "keywords": [ + "self-supervised learning", + "transfer learning / domain adaptation", + "continual learning" + ], + "languages": [ + "french" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.896.pdf", "paper_type": "Main-Poster", @@ -57637,7 +61601,11 @@ ], "id": "P4020", "is_paper": true, - "keywords": [], + "keywords": [ + "data influence", + "hardness of samples" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.7.pdf", "paper_type": "Main-Poster", @@ -57676,7 +61644,10 @@ ], "id": "P4021", "is_paper": true, - "keywords": [], + "keywords": [ + "benchmarking" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.218.pdf", "paper_type": "findings", @@ -57710,7 +61681,10 @@ ], "id": "P4026", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.41.pdf", "paper_type": "Main-Poster", @@ -57749,7 +61723,13 @@ ], "id": "P4028", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "language resources", + "nlp datasets", + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.378.pdf", "paper_type": "findings", @@ -57781,7 +61761,14 @@ ], "id": "P4032", "is_paper": true, - "keywords": [], + "keywords": [ + "data ethics", + "human factors in nlp", + "ethical considerations in nlp applications" + ], + "languages": [ + "hindi" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.842.pdf", "paper_type": "findings", @@ -57811,7 +61798,12 @@ ], "id": "P4037", "is_paper": true, - "keywords": [], + "keywords": [ + "argument mining" + ], + "languages": [ + "russian" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.209.pdf", "paper_type": "findings", @@ -57845,7 +61837,10 @@ ], "id": "P404", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.853.pdf", "paper_type": "Main-Poster", @@ -57886,7 +61881,10 @@ ], "id": "P4049", "is_paper": true, - "keywords": [], + "keywords": [ + "entity linking/disambiguation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.497.pdf", "paper_type": "findings", @@ -57922,7 +61920,10 @@ ], "id": "P4051", "is_paper": true, - "keywords": [], + "keywords": [ + "topic modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.742.pdf", "paper_type": "findings", @@ -57955,7 +61956,10 @@ ], "id": "P4053", "is_paper": true, - "keywords": [], + "keywords": [ + "emoji prediction and analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.152.pdf", "paper_type": "findings", @@ -57988,7 +61992,11 @@ ], "id": "P4055", "is_paper": true, - "keywords": [], + "keywords": [ + "counterfactual/contrastive explanations", + "feature attribution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.842.pdf", "paper_type": "Main-Oral", @@ -58022,7 +62030,11 @@ ], "id": "P4056", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation", + "reflections and critiques" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.118.pdf", "paper_type": "Main-Poster", @@ -58054,7 +62066,10 @@ ], "id": "P4058", "is_paper": true, - "keywords": [], + "keywords": [ + "metaphor" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.186.pdf", "paper_type": "Main-Poster", @@ -58092,7 +62107,10 @@ ], "id": "P406", "is_paper": true, - "keywords": [], + "keywords": [ + "document representation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.188.pdf", "paper_type": "findings", @@ -58126,7 +62144,10 @@ ], "id": "P4064", "is_paper": true, - "keywords": [], + "keywords": [ + "lessons from other fields" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.383.pdf", "paper_type": "findings", @@ -58159,7 +62180,12 @@ ], "id": "P4065", "is_paper": true, - "keywords": [], + "keywords": [ + "linguistic variation", + "multilingual pre-training", + "dialects and language varieties" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.51.pdf", "paper_type": "findings", @@ -58193,7 +62219,11 @@ ], "id": "P4069", "is_paper": true, - "keywords": [], + "keywords": [ + "dialogue", + "conversation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.385.pdf", "paper_type": "findings", @@ -58227,7 +62257,11 @@ ], "id": "P4074", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial attacks/examples/training", + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.138.pdf", "paper_type": "findings", @@ -58259,7 +62293,10 @@ ], "id": "P4079", "is_paper": true, - "keywords": [], + "keywords": [ + "bridging resolution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.383.pdf", "paper_type": "Main-Oral", @@ -58294,6 +62331,7 @@ "id": "P4086", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.26.pdf", "paper_type": "findings", @@ -58327,7 +62365,28 @@ ], "id": "P4087", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "multilingual corpora", + "nlp datasets", + "datasets for low resource languages" + ], + "languages": [ + "assamese", + "bhojpuri", + "bengali", + "gujarati", + "hindi", + "kannada", + "malayalam", + "marathi", + "nepali", + "oriya", + "punjabi", + "tamil", + "telugu", + "urdu" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.215.pdf", "paper_type": "findings", @@ -58359,7 +62418,12 @@ ], "id": "P4089", "is_paper": true, - "keywords": [], + "keywords": [ + "morphological inflection", + "phonology", + "grapheme-to-phoneme conversion" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.54.pdf", "paper_type": "Main-Poster", @@ -58393,7 +62457,10 @@ ], "id": "P4090", "is_paper": true, - "keywords": [], + "keywords": [ + "mathematical nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.379.pdf", "paper_type": "Main-Poster", @@ -58426,7 +62493,11 @@ ], "id": "P4101", "is_paper": true, - "keywords": [], + "keywords": [ + "ai hype & expectations", + "forgotten lessons" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.341.pdf", "paper_type": "Main-Poster", @@ -58459,7 +62530,10 @@ ], "id": "P4105", "is_paper": true, - "keywords": [], + "keywords": [ + "open-domain qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.467.pdf", "paper_type": "Main-Poster", @@ -58494,6 +62568,7 @@ "id": "P4106", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.678.pdf", "paper_type": "Main-Poster", @@ -58527,7 +62602,10 @@ ], "id": "P411", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.116.pdf", "paper_type": "Main-Poster", @@ -58559,7 +62637,14 @@ ], "id": "P4115", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "benchmarking", + "nlp datasets" + ], + "languages": [ + "german" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.908.pdf", "paper_type": "Main-Poster", @@ -58596,7 +62681,11 @@ ], "id": "P4116", "is_paper": true, - "keywords": [], + "keywords": [ + "human-computer interaction", + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.555.pdf", "paper_type": "Main-Oral", @@ -58631,7 +62720,18 @@ ], "id": "P4124", "is_paper": true, - "keywords": [], + "keywords": [ + "biases", + "efficient mt training", + "pre-training for mt" + ], + "languages": [ + "german", + "burmese", + "indonesian", + "turkish", + "tagalog" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.512.pdf", "paper_type": "findings", @@ -58665,7 +62765,10 @@ ], "id": "P4130", "is_paper": true, - "keywords": [], + "keywords": [ + "misinformation detection and analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.286.pdf", "paper_type": "Main-Poster", @@ -58701,7 +62804,10 @@ ], "id": "P4132", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.598.pdf", "paper_type": "findings", @@ -58735,7 +62841,11 @@ ], "id": "P4135", "is_paper": true, - "keywords": [], + "keywords": [ + "sociolinguistics", + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.433.pdf", "paper_type": "findings", @@ -58769,7 +62879,10 @@ ], "id": "P4136", "is_paper": true, - "keywords": [], + "keywords": [ + "analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.80.pdf", "paper_type": "Main-Poster", @@ -58799,7 +62912,10 @@ ], "id": "P4139", "is_paper": true, - "keywords": [], + "keywords": [ + "subword representations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.326.pdf", "paper_type": "Main-Oral", @@ -58833,7 +62949,12 @@ ], "id": "P4140", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal pretraining", + "image text matching", + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.846.pdf", "paper_type": "findings", @@ -58868,7 +62989,10 @@ ], "id": "P415", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.551.pdf", "paper_type": "findings", @@ -58901,6 +63025,7 @@ "id": "P4152", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.184.pdf", "paper_type": "findings", @@ -58931,7 +63056,10 @@ ], "id": "P4153", "is_paper": true, - "keywords": [], + "keywords": [ + "speech translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.816.pdf", "paper_type": "findings", @@ -58966,7 +63094,10 @@ ], "id": "P4155", "is_paper": true, - "keywords": [], + "keywords": [ + "efficient models" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.22.pdf", "paper_type": "Main-Poster", @@ -58999,7 +63130,10 @@ ], "id": "P4157", "is_paper": true, - "keywords": [], + "keywords": [ + "inference methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.517.pdf", "paper_type": "Main-Poster", @@ -59034,7 +63168,10 @@ ], "id": "P4158", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal content generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.603.pdf", "paper_type": "findings", @@ -59066,7 +63203,10 @@ ], "id": "P4159", "is_paper": true, - "keywords": [], + "keywords": [ + "generalization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.590.pdf", "paper_type": "Main-Poster", @@ -59100,7 +63240,10 @@ ], "id": "P4162", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.478.pdf", "paper_type": "Main-Poster", @@ -59131,7 +63274,12 @@ ], "id": "P4171", "is_paper": true, - "keywords": [], + "keywords": [ + "cognitive modeling" + ], + "languages": [ + "french" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.175.pdf", "paper_type": "Main-Oral", @@ -59164,7 +63312,11 @@ ], "id": "P4178", "is_paper": true, - "keywords": [], + "keywords": [ + "efficient models", + "model architectures" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.353.pdf", "paper_type": "Main-Poster", @@ -59198,7 +63350,11 @@ ], "id": "P4181", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction", + "zero/few-shot extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.440.pdf", "paper_type": "Main-Poster", @@ -59230,7 +63386,10 @@ ], "id": "P4182", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.111.pdf", "paper_type": "Main-Poster", @@ -59262,7 +63421,12 @@ ], "id": "P4186", "is_paper": true, - "keywords": [], + "keywords": [ + "human evaluation", + "data-to-text generation", + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.401.pdf", "paper_type": "Main-Oral", @@ -59296,7 +63460,10 @@ ], "id": "P4188", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.655.pdf", "paper_type": "Main-Oral", @@ -59331,7 +63498,10 @@ ], "id": "P4192", "is_paper": true, - "keywords": [], + "keywords": [ + "factuality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.844.pdf", "paper_type": "Main-Poster", @@ -59369,7 +63539,11 @@ ], "id": "P4196", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "zero/few-shot extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.369.pdf", "paper_type": "Main-Poster", @@ -59400,7 +63574,10 @@ ], "id": "P4198", "is_paper": true, - "keywords": [], + "keywords": [ + "parsing algorighms (symbolic, theoritical results)" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.89.pdf", "paper_type": "Main-Poster", @@ -59435,7 +63612,10 @@ ], "id": "P420", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.727.pdf", "paper_type": "Main-Poster", @@ -59469,7 +63649,11 @@ ], "id": "P4202", "is_paper": true, - "keywords": [], + "keywords": [ + "constituency parsing", + "parsing algorighms (symbolic, theoritical results)" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.204.pdf", "paper_type": "Main-Poster", @@ -59507,7 +63691,10 @@ ], "id": "P4207", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.525.pdf", "paper_type": "Main-Oral", @@ -59538,7 +63725,22 @@ ], "id": "P4208", "is_paper": true, - "keywords": [], + "keywords": [ + "less-resourced languages" + ], + "languages": [ + "azeri turkish", + "mazanderani", + "gilaki", + "sindhi", + "kashmiri", + "central kurdish", + "northern kurdish", + "gorani", + "persian", + "arabic", + "urdu" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.809.pdf", "paper_type": "Main-Oral", @@ -59571,7 +63773,10 @@ ], "id": "P4209", "is_paper": true, - "keywords": [], + "keywords": [ + "commonsense qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.33.pdf", "paper_type": "Main-Poster", @@ -59611,7 +63816,10 @@ ], "id": "P4210", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge base construction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.642.pdf", "paper_type": "findings", @@ -59643,7 +63851,10 @@ ], "id": "P4213", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal pretraining" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.31.pdf", "paper_type": "Main-Poster", @@ -59679,7 +63890,10 @@ ], "id": "P4215", "is_paper": true, - "keywords": [], + "keywords": [ + "open information extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.273.pdf", "paper_type": "Main-Poster", @@ -59713,7 +63927,10 @@ ], "id": "P4220", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial attacks/examples/training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.867.pdf", "paper_type": "Main-Poster", @@ -59746,7 +63963,10 @@ ], "id": "P4221", "is_paper": true, - "keywords": [], + "keywords": [ + "dense retrieval" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.99.pdf", "paper_type": "Main-Oral", @@ -59779,7 +63999,11 @@ ], "id": "P4225", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation", + "long-form summarization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.481.pdf", "paper_type": "findings", @@ -59812,7 +64036,10 @@ ], "id": "P4226", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.143.pdf", "paper_type": "Main-Poster", @@ -59846,7 +64073,13 @@ ], "id": "P4228", "is_paper": true, - "keywords": [], + "keywords": [ + "semantic parsing", + "interpretability", + "reasoning", + "math qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.579.pdf", "paper_type": "findings", @@ -59884,6 +64117,7 @@ "id": "P4230", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.537.pdf", "paper_type": "findings", @@ -59918,7 +64152,10 @@ ], "id": "P4233", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.312.pdf", "paper_type": "Main-Poster", @@ -59953,7 +64190,10 @@ ], "id": "P4234", "is_paper": true, - "keywords": [], + "keywords": [ + "free-text/natural language explanations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.112.pdf", "paper_type": "Main-Poster", @@ -59988,7 +64228,10 @@ ], "id": "P4238", "is_paper": true, - "keywords": [], + "keywords": [ + "continual learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.427.pdf", "paper_type": "Main-Poster", @@ -60024,7 +64267,10 @@ ], "id": "P424", "is_paper": true, - "keywords": [], + "keywords": [ + "data-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.281.pdf", "paper_type": "Main-Poster", @@ -60058,7 +64304,10 @@ ], "id": "P4242", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.150.pdf", "paper_type": "findings", @@ -60094,7 +64343,10 @@ ], "id": "P4250", "is_paper": true, - "keywords": [], + "keywords": [ + "online adaptation for mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.126.pdf", "paper_type": "Main-Poster", @@ -60130,7 +64382,11 @@ ], "id": "P4252", "is_paper": true, - "keywords": [], + "keywords": [ + "human behavior analysis", + "sociolinguistics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.410.pdf", "paper_type": "findings", @@ -60164,7 +64420,10 @@ ], "id": "P4253", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.443.pdf", "paper_type": "findings", @@ -60198,7 +64457,11 @@ ], "id": "P4254", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot learning", + "meta learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.454.pdf", "paper_type": "Main-Poster", @@ -60231,7 +64494,10 @@ ], "id": "P4258", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.109.pdf", "paper_type": "Main-Poster", @@ -60264,7 +64530,10 @@ ], "id": "P426", "is_paper": true, - "keywords": [], + "keywords": [ + "feature attribution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.798.pdf", "paper_type": "findings", @@ -60297,7 +64566,12 @@ ], "id": "P4260", "is_paper": true, - "keywords": [], + "keywords": [ + "hate-speech detection", + "sociolinguistics", + "quantiative analyses of news and/or social media" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.845.pdf", "paper_type": "Main-Poster", @@ -60330,6 +64604,7 @@ "id": "P4263", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.622.pdf", "paper_type": "Main-Poster", @@ -60363,7 +64638,10 @@ ], "id": "P4268", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.140.pdf", "paper_type": "Main-Poster", @@ -60399,7 +64677,10 @@ ], "id": "P427", "is_paper": true, - "keywords": [], + "keywords": [ + "factuality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.275.pdf", "paper_type": "findings", @@ -60434,7 +64715,10 @@ ], "id": "P4277", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.310.pdf", "paper_type": "findings", @@ -60467,7 +64751,10 @@ ], "id": "P4280", "is_paper": true, - "keywords": [], + "keywords": [ + "extractive summarisation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.531.pdf", "paper_type": "Main-Poster", @@ -60501,7 +64788,10 @@ ], "id": "P4282", "is_paper": true, - "keywords": [], + "keywords": [ + "human behavior analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.735.pdf", "paper_type": "Main-Poster", @@ -60536,7 +64826,10 @@ ], "id": "P4286", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.632.pdf", "paper_type": "Main-Oral", @@ -60573,7 +64866,13 @@ ], "id": "P4289", "is_paper": true, - "keywords": [], + "keywords": [ + "interactive storytelling", + "applications", + "grounded dialog", + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.624.pdf", "paper_type": "Main-Poster", @@ -60608,7 +64907,12 @@ ], "id": "P4295", "is_paper": true, - "keywords": [], + "keywords": [ + "anaphora resolution", + "coreference resolution", + "bridging resolution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.788.pdf", "paper_type": "findings", @@ -60641,7 +64945,10 @@ ], "id": "P4296", "is_paper": true, - "keywords": [], + "keywords": [ + "(non-)reproducibility" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.568.pdf", "paper_type": "Main-Poster", @@ -60676,7 +64983,11 @@ ], "id": "P430", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "document-level extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.889.pdf", "paper_type": "Main-Oral", @@ -60708,7 +65019,11 @@ ], "id": "P4308", "is_paper": true, - "keywords": [], + "keywords": [ + "compositionality", + "word embeddings" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.863.pdf", "paper_type": "Main-Poster", @@ -60740,7 +65055,12 @@ ], "id": "P4315", "is_paper": true, - "keywords": [], + "keywords": [ + "less-resourced languages", + "endangered languages", + "indigenous languages" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.87.pdf", "paper_type": "Main-Oral", @@ -60785,7 +65105,10 @@ ], "id": "P4317", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.501.pdf", "paper_type": "Main-Poster", @@ -60817,7 +65140,10 @@ ], "id": "P432", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.129.pdf", "paper_type": "findings", @@ -60848,7 +65174,10 @@ ], "id": "P4326", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge graphs" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.158.pdf", "paper_type": "Main-Poster", @@ -60881,7 +65210,11 @@ ], "id": "P4328", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "zero/few-shot extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.414.pdf", "paper_type": "Main-Oral", @@ -60959,7 +65292,36 @@ ], "id": "P433", "is_paper": true, - "keywords": [], + "keywords": [ + "benchmarking", + "language resources", + "multilingual corpora", + "nlp datasets", + "automatic evaluation of datasets", + "evaluation", + "reproducibility" + ], + "languages": [ + "indonesian", + "banjar", + "batak toba", + "madura", + "ngaju", + "lampung nyo", + "batak karo", + "tok pisin", + "tetun dili", + "dayak", + "khek", + "tiociu", + "minangkabau", + "acehnese", + "buginese", + "sundanese", + "javenese", + "balinese", + "batak" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.868.pdf", "paper_type": "findings", @@ -60997,7 +65359,10 @@ ], "id": "P4334", "is_paper": true, - "keywords": [], + "keywords": [ + "passage retrieval" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.139.pdf", "paper_type": "Main-Poster", @@ -61031,7 +65396,16 @@ ], "id": "P4335", "is_paper": true, - "keywords": [], + "keywords": [ + "stance detection" + ], + "languages": [ + "french", + "german", + "portuguese", + "dutch", + "romanian" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.461.pdf", "paper_type": "Main-Poster", @@ -61064,7 +65438,10 @@ ], "id": "P4336", "is_paper": true, - "keywords": [], + "keywords": [ + "benchmarking" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.609.pdf", "paper_type": "findings", @@ -61102,7 +65479,11 @@ ], "id": "P4340", "is_paper": true, - "keywords": [], + "keywords": [ + "free-text/natural language explanations", + "human-subject application-grounded evaluations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.392.pdf", "paper_type": "Main-Oral", @@ -61138,7 +65519,10 @@ ], "id": "P4341", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodal applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.473.pdf", "paper_type": "findings", @@ -61173,7 +65557,10 @@ ], "id": "P4348", "is_paper": true, - "keywords": [], + "keywords": [ + "domain adaptation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.602.pdf", "paper_type": "findings", @@ -61206,7 +65593,10 @@ ], "id": "P4350", "is_paper": true, - "keywords": [], + "keywords": [ + "semantic parsing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.15.pdf", "paper_type": "Main-Poster", @@ -61241,7 +65631,10 @@ ], "id": "P4357", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.837.pdf", "paper_type": "Main-Poster", @@ -61275,7 +65668,10 @@ ], "id": "P437", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.635.pdf", "paper_type": "Main-Poster", @@ -61308,7 +65704,11 @@ ], "id": "P4371", "is_paper": true, - "keywords": [], + "keywords": [ + "graph-based methods", + "continual learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.490.pdf", "paper_type": "findings", @@ -61344,7 +65744,11 @@ ], "id": "P4372", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.723.pdf", "paper_type": "Main-Oral", @@ -61381,7 +65785,11 @@ ], "id": "P4382", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge augmented", + "commonsense reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.362.pdf", "paper_type": "Main-Poster", @@ -61418,7 +65826,10 @@ ], "id": "P4384", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.421.pdf", "paper_type": "findings", @@ -61450,7 +65861,10 @@ ], "id": "P4387", "is_paper": true, - "keywords": [], + "keywords": [ + "paraphrase generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.659.pdf", "paper_type": "findings", @@ -61484,7 +65898,19 @@ ], "id": "P4395", "is_paper": true, - "keywords": [], + "keywords": [ + "less-resourced languages", + "endangered languages", + "indigenous languages", + "minoritized languages", + "language documentation", + "resources for less-resourced languages", + "software and tools" + ], + "languages": [ + "517 african languages", + "language varieties" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.97.pdf", "paper_type": "findings", @@ -61522,7 +65948,10 @@ ], "id": "P4396", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.343.pdf", "paper_type": "Main-Poster", @@ -61555,7 +65984,10 @@ ], "id": "P44", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodal applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.139.pdf", "paper_type": "Main-Poster", @@ -61587,7 +66019,12 @@ ], "id": "P4406", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction", + "document-level extraction", + "zero/few-shot extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.446.pdf", "paper_type": "Main-Poster", @@ -61619,7 +66056,10 @@ ], "id": "P4408", "is_paper": true, - "keywords": [], + "keywords": [ + "mathematical nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.340.pdf", "paper_type": "Main-Poster", @@ -61650,7 +66090,10 @@ ], "id": "P4409", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.389.pdf", "paper_type": "Main-Poster", @@ -61690,7 +66133,10 @@ ], "id": "P4415", "is_paper": true, - "keywords": [], + "keywords": [ + "reinforcement learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.373.pdf", "paper_type": "Main-Poster", @@ -61729,7 +66175,10 @@ ], "id": "P4416", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.513.pdf", "paper_type": "Main-Poster", @@ -61762,7 +66211,12 @@ ], "id": "P4419", "is_paper": true, - "keywords": [], + "keywords": [ + "spoken dialogue systems", + "task-oriented", + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.854.pdf", "paper_type": "Main-Poster", @@ -61795,7 +66249,10 @@ ], "id": "P442", "is_paper": true, - "keywords": [], + "keywords": [ + "efficient models" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.818.pdf", "paper_type": "Main-Poster", @@ -61831,7 +66288,10 @@ ], "id": "P4422", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.83.pdf", "paper_type": "findings", @@ -61867,7 +66327,11 @@ ], "id": "P4423", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation", + "ai hype & expectations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.29.pdf", "paper_type": "findings", @@ -61900,7 +66364,13 @@ ], "id": "P4424", "is_paper": true, - "keywords": [], + "keywords": [ + "multihop qa", + "reasoning", + "few-shot qa", + "open-domain qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.557.pdf", "paper_type": "Main-Poster", @@ -61933,7 +66403,10 @@ ], "id": "P4433", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation methodologies" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.154.pdf", "paper_type": "Main-Poster", @@ -61970,7 +66443,10 @@ ], "id": "P4435", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.60.pdf", "paper_type": "findings", @@ -62003,7 +66479,10 @@ ], "id": "P4442", "is_paper": true, - "keywords": [], + "keywords": [ + "generalization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.38.pdf", "paper_type": "Main-Poster", @@ -62035,7 +66514,12 @@ ], "id": "P4446", "is_paper": true, - "keywords": [], + "keywords": [ + "data augmentation", + "generalization", + "human-in-the-loop / active learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.474.pdf", "paper_type": "Main-Poster", @@ -62068,7 +66552,13 @@ ], "id": "P4448", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation", + "conversational summarization", + "long-form summarization", + "architectures" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.871.pdf", "paper_type": "findings", @@ -62101,7 +66591,10 @@ ], "id": "P4451", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.549.pdf", "paper_type": "findings", @@ -62133,7 +66626,10 @@ ], "id": "P4456", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.499.pdf", "paper_type": "findings", @@ -62169,7 +66665,10 @@ ], "id": "P4462", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.654.pdf", "paper_type": "Main-Poster", @@ -62204,7 +66703,10 @@ ], "id": "P4463", "is_paper": true, - "keywords": [], + "keywords": [ + "language/cultural bias analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.359.pdf", "paper_type": "Main-Poster", @@ -62241,7 +66743,10 @@ ], "id": "P4466", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal content generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.465.pdf", "paper_type": "findings", @@ -62277,7 +66782,10 @@ ], "id": "P4470", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial attacks/examples/training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.781.pdf", "paper_type": "Main-Poster", @@ -62310,7 +66818,10 @@ ], "id": "P4471", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.397.pdf", "paper_type": "Main-Poster", @@ -62344,7 +66855,10 @@ ], "id": "P4481", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.203.pdf", "paper_type": "Main-Poster", @@ -62385,7 +66899,10 @@ ], "id": "P4482", "is_paper": true, - "keywords": [], + "keywords": [ + "contrastive learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.355.pdf", "paper_type": "Main-Poster", @@ -62417,7 +66934,10 @@ ], "id": "P4484", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation and metrics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.839.pdf", "paper_type": "Main-Poster", @@ -62448,7 +66968,10 @@ ], "id": "P4490", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.125.pdf", "paper_type": "Main-Poster", @@ -62481,7 +67004,10 @@ ], "id": "P4494", "is_paper": true, - "keywords": [], + "keywords": [ + "inference methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.692.pdf", "paper_type": "Main-Poster", @@ -62515,7 +67041,12 @@ ], "id": "P4495", "is_paper": true, - "keywords": [], + "keywords": [ + "textual entailment", + "natural language inference", + "reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.302.pdf", "paper_type": "Main-Poster", @@ -62552,7 +67083,10 @@ ], "id": "P4497", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual pre-training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.856.pdf", "paper_type": "Main-Poster", @@ -62585,7 +67119,10 @@ ], "id": "P4500", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.248.pdf", "paper_type": "findings", @@ -62617,7 +67154,12 @@ ], "id": "P4501", "is_paper": true, - "keywords": [], + "keywords": [ + "multihop qa", + "interpretability", + "reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.642.pdf", "paper_type": "Main-Poster", @@ -62652,7 +67194,12 @@ ], "id": "P4503", "is_paper": true, - "keywords": [], + "keywords": [ + "scaling", + "retrieval-augmented models", + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.546.pdf", "paper_type": "Main-Oral", @@ -62685,7 +67232,11 @@ ], "id": "P4505", "is_paper": true, - "keywords": [], + "keywords": [ + "video processing", + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.861.pdf", "paper_type": "Main-Poster", @@ -62721,7 +67272,10 @@ ], "id": "P4509", "is_paper": true, - "keywords": [], + "keywords": [ + "contrastive learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.761.pdf", "paper_type": "findings", @@ -62758,7 +67312,11 @@ ], "id": "P451", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability", + "table qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.334.pdf", "paper_type": "Main-Poster", @@ -62789,7 +67347,10 @@ ], "id": "P4513", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.452.pdf", "paper_type": "Main-Poster", @@ -62822,7 +67383,13 @@ ], "id": "P4517", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation", + "model bias/unfairness mitigation", + "participatory/community-based nlp", + "ethical considerations in nlp applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.507.pdf", "paper_type": "Main-Oral", @@ -62854,7 +67421,11 @@ ], "id": "P4519", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.141.pdf", "paper_type": "findings", @@ -62889,7 +67460,10 @@ ], "id": "P452", "is_paper": true, - "keywords": [], + "keywords": [ + "open-domain qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.768.pdf", "paper_type": "findings", @@ -62925,7 +67499,11 @@ ], "id": "P4521", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-document summarization", + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.549.pdf", "paper_type": "Main-Poster", @@ -62959,7 +67537,10 @@ ], "id": "P4522", "is_paper": true, - "keywords": [], + "keywords": [ + "textual entailment" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.76.pdf", "paper_type": "Main-Poster", @@ -62993,7 +67574,12 @@ ], "id": "P4524", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "retrieval-augmented models", + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.576.pdf", "paper_type": "findings", @@ -63027,7 +67613,11 @@ ], "id": "P4526", "is_paper": true, - "keywords": [], + "keywords": [ + "benchmarking", + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.784.pdf", "paper_type": "Main-Oral", @@ -63061,7 +67651,12 @@ ], "id": "P4529", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "scaling", + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.631.pdf", "paper_type": "Main-Poster", @@ -63097,7 +67692,11 @@ ], "id": "P4532", "is_paper": true, - "keywords": [], + "keywords": [ + "efficient mt training", + "few-shot/zero-shot mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.105.pdf", "paper_type": "Main-Poster", @@ -63128,7 +67727,10 @@ ], "id": "P4536", "is_paper": true, - "keywords": [], + "keywords": [ + "transfer learning / domain adaptation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.333.pdf", "paper_type": "findings", @@ -63164,6 +67766,7 @@ "id": "P4541", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.172.pdf", "paper_type": "Main-Poster", @@ -63198,7 +67801,12 @@ ], "id": "P4543", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training", + "scaling", + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.326.pdf", "paper_type": "findings", @@ -63233,7 +67841,10 @@ ], "id": "P4544", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.779.pdf", "paper_type": "findings", @@ -63264,7 +67875,17 @@ ], "id": "P4549", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal content generation" + ], + "languages": [ + "spanish", + "german", + "chinese", + "japanese", + "hebrew", + "indonesian" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.266.pdf", "paper_type": "Main-Poster", @@ -63302,7 +67923,12 @@ ], "id": "P4552", "is_paper": true, - "keywords": [], + "keywords": [ + "passage retrieval", + "dense retrieval", + "re-ranking" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.225.pdf", "paper_type": "findings", @@ -63336,7 +67962,10 @@ ], "id": "P4553", "is_paper": true, - "keywords": [], + "keywords": [ + "open information extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.53.pdf", "paper_type": "Main-Poster", @@ -63371,7 +68000,10 @@ ], "id": "P4557", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge graphs" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.900.pdf", "paper_type": "findings", @@ -63405,7 +68037,10 @@ ], "id": "P4558", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.615.pdf", "paper_type": "Main-Poster", @@ -63441,7 +68076,12 @@ ], "id": "P4559", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability/analysis", + "robustness", + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.242.pdf", "paper_type": "Main-Poster", @@ -63473,7 +68113,11 @@ ], "id": "P456", "is_paper": true, - "keywords": [], + "keywords": [ + "domain adaptation", + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.595.pdf", "paper_type": "findings", @@ -63506,7 +68150,11 @@ ], "id": "P4565", "is_paper": true, - "keywords": [], + "keywords": [ + "language resources", + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.113.pdf", "paper_type": "Main-Poster", @@ -63544,6 +68192,7 @@ "id": "P4570", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.620.pdf", "paper_type": "Main-Poster", @@ -63575,7 +68224,11 @@ ], "id": "P4577", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.106.pdf", "paper_type": "Main-Poster", @@ -63606,7 +68259,11 @@ ], "id": "P4581", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation methodologies", + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.142.pdf", "paper_type": "Main-Poster", @@ -63638,7 +68295,11 @@ ], "id": "P4582", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation", + "human factors in nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.293.pdf", "paper_type": "Main-Poster", @@ -63671,7 +68332,10 @@ ], "id": "P4584", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot/zero-shot mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.90.pdf", "paper_type": "Main-Poster", @@ -63707,7 +68371,10 @@ ], "id": "P4585", "is_paper": true, - "keywords": [], + "keywords": [ + "retrieval" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.174.pdf", "paper_type": "Main-Poster", @@ -63743,7 +68410,10 @@ ], "id": "P4590", "is_paper": true, - "keywords": [], + "keywords": [ + "financial/business nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.4.pdf", "paper_type": "Main-Poster", @@ -63774,7 +68444,10 @@ ], "id": "P4593", "is_paper": true, - "keywords": [], + "keywords": [ + "metrics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.80.pdf", "paper_type": "Main-Poster", @@ -63805,7 +68478,10 @@ ], "id": "P4613", "is_paper": true, - "keywords": [], + "keywords": [ + "grammar and knowledge-based approaches" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.163.pdf", "paper_type": "Main-Poster", @@ -63837,7 +68513,10 @@ ], "id": "P4614", "is_paper": true, - "keywords": [], + "keywords": [ + "efficient models" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.18.pdf", "paper_type": "Main-Poster", @@ -63870,7 +68549,10 @@ ], "id": "P4619", "is_paper": true, - "keywords": [], + "keywords": [ + "(non-)reproducibility" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.809.pdf", "paper_type": "findings", @@ -63903,7 +68585,10 @@ ], "id": "P4627", "is_paper": true, - "keywords": [], + "keywords": [ + "extractive summarisation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.802.pdf", "paper_type": "findings", @@ -63937,7 +68622,10 @@ ], "id": "P4634", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.161.pdf", "paper_type": "findings", @@ -63973,7 +68661,10 @@ ], "id": "P4641", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.579.pdf", "paper_type": "Main-Poster", @@ -64010,7 +68701,10 @@ ], "id": "P4660", "is_paper": true, - "keywords": [], + "keywords": [ + "multihop qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.814.pdf", "paper_type": "Main-Poster", @@ -64047,7 +68741,10 @@ ], "id": "P4662", "is_paper": true, - "keywords": [], + "keywords": [ + "code generation and understanding" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.117.pdf", "paper_type": "Main-Poster", @@ -64080,7 +68777,10 @@ ], "id": "P467", "is_paper": true, - "keywords": [], + "keywords": [ + "graph-based methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.16.pdf", "paper_type": "Main-Poster", @@ -64114,7 +68814,10 @@ ], "id": "P4674", "is_paper": true, - "keywords": [], + "keywords": [ + "financial/business nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.329.pdf", "paper_type": "findings", @@ -64146,7 +68849,10 @@ ], "id": "P4675", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.593.pdf", "paper_type": "findings", @@ -64181,7 +68887,12 @@ ], "id": "P4679", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic speech recognition", + "speech technologies", + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.682.pdf", "paper_type": "findings", @@ -64216,7 +68927,10 @@ ], "id": "P468", "is_paper": true, - "keywords": [], + "keywords": [ + "argument mining" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.101.pdf", "paper_type": "Main-Poster", @@ -64248,7 +68962,11 @@ ], "id": "P4680", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation", + "ethical considerations in nlp applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.84.pdf", "paper_type": "Main-Poster", @@ -64284,7 +69002,10 @@ ], "id": "P4681", "is_paper": true, - "keywords": [], + "keywords": [ + "open-domain qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.679.pdf", "paper_type": "findings", @@ -64319,7 +69040,13 @@ ], "id": "P4682", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "multilingual corpora", + "nlp datasets", + "datasets for low resource languages" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.159.pdf", "paper_type": "findings", @@ -64353,7 +69080,10 @@ ], "id": "P4687", "is_paper": true, - "keywords": [], + "keywords": [ + "contrastive learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.683.pdf", "paper_type": "Main-Poster", @@ -64389,7 +69119,11 @@ ], "id": "P4689", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal application", + "cross-modal information extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.106.pdf", "paper_type": "findings", @@ -64425,7 +69159,11 @@ ], "id": "P4690", "is_paper": true, - "keywords": [], + "keywords": [ + "explanation faithfulness", + "hardness of samples" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.715.pdf", "paper_type": "Main-Oral", @@ -64457,7 +69195,14 @@ ], "id": "P4691", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "event extraction" + ], + "languages": [ + "engilish", + "japanese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.247.pdf", "paper_type": "Main-Poster", @@ -64491,7 +69236,10 @@ ], "id": "P4694", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge-augmented methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.118.pdf", "paper_type": "Main-Poster", @@ -64524,7 +69272,10 @@ ], "id": "P4704", "is_paper": true, - "keywords": [], + "keywords": [ + "multihop qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.755.pdf", "paper_type": "findings", @@ -64556,7 +69307,11 @@ ], "id": "P4706", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented", + "dialogue state tracking" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.344.pdf", "paper_type": "findings", @@ -64590,7 +69345,10 @@ ], "id": "P4709", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.852.pdf", "paper_type": "Main-Oral", @@ -64626,7 +69384,10 @@ ], "id": "P471", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge base construction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.709.pdf", "paper_type": "findings", @@ -64660,7 +69421,10 @@ ], "id": "P4715", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.216.pdf", "paper_type": "findings", @@ -64696,7 +69460,10 @@ ], "id": "P4717", "is_paper": true, - "keywords": [], + "keywords": [ + "re-ranking" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.94.pdf", "paper_type": "Main-Oral", @@ -64729,7 +69496,10 @@ ], "id": "P4718", "is_paper": true, - "keywords": [], + "keywords": [ + "fact checking, rumour/misinformation detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.374.pdf", "paper_type": "Main-Poster", @@ -64764,7 +69534,10 @@ ], "id": "P472", "is_paper": true, - "keywords": [], + "keywords": [ + "phrase/sentence embedding" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.258.pdf", "paper_type": "Main-Poster", @@ -64803,7 +69576,10 @@ ], "id": "P4722", "is_paper": true, - "keywords": [], + "keywords": [ + "open-domain qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.263.pdf", "paper_type": "findings", @@ -64838,7 +69614,10 @@ ], "id": "P4724", "is_paper": true, - "keywords": [], + "keywords": [ + "morphological inflection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.207.pdf", "paper_type": "findings", @@ -64870,7 +69649,11 @@ ], "id": "P4728", "is_paper": true, - "keywords": [], + "keywords": [ + "cognitive modeling", + "computational psycholinguistics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.149.pdf", "paper_type": "findings", @@ -64903,7 +69686,10 @@ ], "id": "P4729", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.656.pdf", "paper_type": "Main-Poster", @@ -64940,7 +69726,12 @@ ], "id": "P4730", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.1.pdf", "paper_type": "Main-Poster", @@ -64971,7 +69762,10 @@ ], "id": "P474", "is_paper": true, - "keywords": [], + "keywords": [ + "zero/few-shot extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.5.pdf", "paper_type": "Main-Poster", @@ -65005,7 +69799,10 @@ ], "id": "P4744", "is_paper": true, - "keywords": [], + "keywords": [ + "discourse relations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.44.pdf", "paper_type": "findings", @@ -65037,7 +69834,13 @@ ], "id": "P4745", "is_paper": true, - "keywords": [], + "keywords": [ + "extractive summarisation", + "abstractive summarisation", + "query-focused summarization", + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.541.pdf", "paper_type": "Main-Poster", @@ -65074,7 +69877,10 @@ ], "id": "P4746", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.137.pdf", "paper_type": "findings", @@ -65108,7 +69914,10 @@ ], "id": "P4752", "is_paper": true, - "keywords": [], + "keywords": [ + "model architectures" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.488.pdf", "paper_type": "Main-Poster", @@ -65139,7 +69948,10 @@ ], "id": "P4763", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.562.pdf", "paper_type": "Main-Poster", @@ -65173,7 +69985,12 @@ ], "id": "P4769", "is_paper": true, - "keywords": [], + "keywords": [ + "self-supervised learning", + "contrastive learning", + "representation learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.677.pdf", "paper_type": "Main-Oral", @@ -65206,7 +70023,10 @@ ], "id": "P477", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.231.pdf", "paper_type": "Main-Poster", @@ -65238,7 +70058,10 @@ ], "id": "P4772", "is_paper": true, - "keywords": [], + "keywords": [ + "coreference resolution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.855.pdf", "paper_type": "findings", @@ -65271,7 +70094,12 @@ ], "id": "P4776", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation methodologies", + "evaluation", + "metrics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.181.pdf", "paper_type": "Main-Oral", @@ -65303,7 +70131,10 @@ ], "id": "P4782", "is_paper": true, - "keywords": [], + "keywords": [ + "speech translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.436.pdf", "paper_type": "Main-Poster", @@ -65337,7 +70168,11 @@ ], "id": "P4792", "is_paper": true, - "keywords": [], + "keywords": [ + "continual learning", + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.300.pdf", "paper_type": "Main-Poster", @@ -65372,7 +70207,10 @@ ], "id": "P4803", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation and metrics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.719.pdf", "paper_type": "Main-Poster", @@ -65405,7 +70243,10 @@ ], "id": "P481", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.407.pdf", "paper_type": "Main-Oral", @@ -65439,7 +70280,10 @@ ], "id": "P4810", "is_paper": true, - "keywords": [], + "keywords": [ + "benchmarking" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.644.pdf", "paper_type": "findings", @@ -65474,7 +70318,11 @@ ], "id": "P4811", "is_paper": true, - "keywords": [], + "keywords": [ + "data shortcuts/artifacts", + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.32.pdf", "paper_type": "findings", @@ -65510,7 +70358,10 @@ ], "id": "P4813", "is_paper": true, - "keywords": [], + "keywords": [ + "discourse parsing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.710.pdf", "paper_type": "findings", @@ -65548,7 +70399,10 @@ ], "id": "P4814", "is_paper": true, - "keywords": [], + "keywords": [ + "vision question answering" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.65.pdf", "paper_type": "Main-Poster", @@ -65583,7 +70437,10 @@ ], "id": "P4820", "is_paper": true, - "keywords": [], + "keywords": [ + "vision question answering" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.49.pdf", "paper_type": "findings", @@ -65616,7 +70473,12 @@ ], "id": "P4821", "is_paper": true, - "keywords": [], + "keywords": [ + "indigenous languages" + ], + "languages": [ + "indigenous languages of the americas" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.268.pdf", "paper_type": "Main-Poster", @@ -65651,7 +70513,11 @@ ], "id": "P4824", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction", + "document-level extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.720.pdf", "paper_type": "Main-Poster", @@ -65687,7 +70553,27 @@ ], "id": "P4826", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "benchmarking", + "language resources", + "automatic creation and evaluation of language resources", + "nlp datasets", + "datasets for low resource languages" + ], + "languages": [ + "hindi", + "bengali", + "assamese", + "gujarati", + "kannada", + "malayalam", + "marathi", + "tamil", + "telugu", + "punjabi", + "odia" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.582.pdf", "paper_type": "Main-Poster", @@ -65723,7 +70609,10 @@ ], "id": "P4827", "is_paper": true, - "keywords": [], + "keywords": [ + "open-domain qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.131.pdf", "paper_type": "findings", @@ -65755,7 +70644,10 @@ ], "id": "P4835", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.657.pdf", "paper_type": "Main-Poster", @@ -65790,7 +70682,10 @@ ], "id": "P4838", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.18.pdf", "paper_type": "Main-Poster", @@ -65824,7 +70719,10 @@ ], "id": "P4844", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/unfairness mitigation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.812.pdf", "paper_type": "findings", @@ -65857,7 +70755,10 @@ ], "id": "P4858", "is_paper": true, - "keywords": [], + "keywords": [ + "phrase/sentence embedding" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.613.pdf", "paper_type": "Main-Poster", @@ -65891,7 +70792,10 @@ ], "id": "P486", "is_paper": true, - "keywords": [], + "keywords": [ + "model compression methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.471.pdf", "paper_type": "Main-Poster", @@ -65926,7 +70830,10 @@ ], "id": "P4865", "is_paper": true, - "keywords": [], + "keywords": [ + "data-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.160.pdf", "paper_type": "Main-Poster", @@ -65957,7 +70864,10 @@ ], "id": "P4867", "is_paper": true, - "keywords": [], + "keywords": [ + "calibration/uncertainty" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.381.pdf", "paper_type": "Main-Poster", @@ -65991,7 +70901,10 @@ ], "id": "P487", "is_paper": true, - "keywords": [], + "keywords": [ + "lexical semantic change" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.206.pdf", "paper_type": "Main-Poster", @@ -66023,7 +70936,12 @@ ], "id": "P4870", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation", + "inference methods", + "model architectures" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.528.pdf", "paper_type": "Main-Poster", @@ -66057,7 +70975,10 @@ ], "id": "P4872", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.93.pdf", "paper_type": "Main-Poster", @@ -66091,7 +71012,12 @@ ], "id": "P4878", "is_paper": true, - "keywords": [], + "keywords": [ + "data influence", + "feature attribution", + "knowledge tracing/discovering/inducing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.547.pdf", "paper_type": "Main-Poster", @@ -66125,7 +71051,11 @@ ], "id": "P4889", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal application", + "cross-modal information extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.27.pdf", "paper_type": "Main-Poster", @@ -66156,7 +71086,12 @@ ], "id": "P4891", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "benchmarking", + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.171.pdf", "paper_type": "Main-Poster", @@ -66187,7 +71122,10 @@ ], "id": "P4899", "is_paper": true, - "keywords": [], + "keywords": [ + "phrase/sentence embedding" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.882.pdf", "paper_type": "Main-Poster", @@ -66221,7 +71159,10 @@ ], "id": "P4908", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.821.pdf", "paper_type": "Main-Oral", @@ -66252,7 +71193,10 @@ ], "id": "P4914", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/unfairness mitigation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.11.pdf", "paper_type": "Main-Poster", @@ -66286,7 +71230,11 @@ ], "id": "P4915", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "zero/few-shot extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.902.pdf", "paper_type": "Main-Poster", @@ -66317,7 +71265,11 @@ ], "id": "P4916", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.364.pdf", "paper_type": "findings", @@ -66351,7 +71303,12 @@ ], "id": "P4923", "is_paper": true, - "keywords": [], + "keywords": [ + "human behavior analysis", + "hate-speech detection", + "quantiative analyses of news and/or social media" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.616.pdf", "paper_type": "Main-Oral", @@ -66386,7 +71343,10 @@ ], "id": "P4936", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.150.pdf", "paper_type": "Main-Poster", @@ -66421,7 +71381,10 @@ ], "id": "P4937", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.160.pdf", "paper_type": "findings", @@ -66458,7 +71421,10 @@ ], "id": "P495", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.308.pdf", "paper_type": "findings", @@ -66499,7 +71465,10 @@ ], "id": "P4950", "is_paper": true, - "keywords": [], + "keywords": [ + "data-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.894.pdf", "paper_type": "Main-Poster", @@ -66535,7 +71504,10 @@ ], "id": "P4956", "is_paper": true, - "keywords": [], + "keywords": [ + "transfer learning / domain adaptation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.64.pdf", "paper_type": "findings", @@ -66568,7 +71540,10 @@ ], "id": "P496", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.21.pdf", "paper_type": "Main-Poster", @@ -66609,7 +71584,10 @@ ], "id": "P4966", "is_paper": true, - "keywords": [], + "keywords": [ + "speech translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.744.pdf", "paper_type": "Main-Poster", @@ -66644,7 +71622,10 @@ ], "id": "P4968", "is_paper": true, - "keywords": [], + "keywords": [ + "hate-speech detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.318.pdf", "paper_type": "Main-Poster", @@ -66678,7 +71659,10 @@ ], "id": "P4974", "is_paper": true, - "keywords": [], + "keywords": [ + "efficient models" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.162.pdf", "paper_type": "Main-Poster", @@ -66710,7 +71694,10 @@ ], "id": "P4975", "is_paper": true, - "keywords": [], + "keywords": [ + "data influence" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.643.pdf", "paper_type": "Main-Poster", @@ -66743,7 +71730,10 @@ ], "id": "P4980", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.394.pdf", "paper_type": "Main-Poster", @@ -66779,7 +71769,10 @@ ], "id": "P4985", "is_paper": true, - "keywords": [], + "keywords": [ + "spoken dialogue systems" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.167.pdf", "paper_type": "Main-Poster", @@ -66814,7 +71807,10 @@ ], "id": "P4993", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.708.pdf", "paper_type": "Main-Oral", @@ -66845,7 +71841,10 @@ ], "id": "P50", "is_paper": true, - "keywords": [], + "keywords": [ + "linguistic theories" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.864.pdf", "paper_type": "Main-Poster", @@ -66877,7 +71876,10 @@ ], "id": "P501", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.701.pdf", "paper_type": "Main-Poster", @@ -66914,7 +71916,10 @@ ], "id": "P5015", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.790.pdf", "paper_type": "findings", @@ -66948,7 +71953,10 @@ ], "id": "P5017", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.30.pdf", "paper_type": "Main-Poster", @@ -66980,7 +71988,10 @@ ], "id": "P5024", "is_paper": true, - "keywords": [], + "keywords": [ + "language/cultural bias analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.749.pdf", "paper_type": "findings", @@ -67014,7 +72025,10 @@ ], "id": "P5026", "is_paper": true, - "keywords": [], + "keywords": [ + "document-level extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.532.pdf", "paper_type": "Main-Poster", @@ -67046,7 +72060,11 @@ ], "id": "P5031", "is_paper": true, - "keywords": [], + "keywords": [ + "style analysis", + "style generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.243.pdf", "paper_type": "findings", @@ -67082,7 +72100,10 @@ ], "id": "P5041", "is_paper": true, - "keywords": [], + "keywords": [ + "code generation and understanding" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.540.pdf", "paper_type": "Main-Poster", @@ -67114,7 +72135,12 @@ ], "id": "P5043", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "open information extraction", + "zero/few-shot extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.409.pdf", "paper_type": "Main-Oral", @@ -67148,7 +72174,10 @@ ], "id": "P5048", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.690.pdf", "paper_type": "Main-Poster", @@ -67184,7 +72213,11 @@ ], "id": "P505", "is_paper": true, - "keywords": [], + "keywords": [ + "efficient inference for mt", + "parallel decoding/non-autoregressive mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.689.pdf", "paper_type": "Main-Poster", @@ -67220,7 +72253,10 @@ ], "id": "P5057", "is_paper": true, - "keywords": [], + "keywords": [ + "open-domain qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.424.pdf", "paper_type": "Main-Poster", @@ -67255,7 +72291,10 @@ ], "id": "P5058", "is_paper": true, - "keywords": [], + "keywords": [ + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.85.pdf", "paper_type": "Main-Poster", @@ -67289,7 +72328,12 @@ ], "id": "P5059", "is_paper": true, - "keywords": [], + "keywords": [ + "sociolinguistics" + ], + "languages": [ + "mandarin chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.314.pdf", "paper_type": "findings", @@ -67325,7 +72369,10 @@ ], "id": "P5065", "is_paper": true, - "keywords": [], + "keywords": [ + "calibration/uncertainty" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.652.pdf", "paper_type": "Main-Poster", @@ -67361,7 +72408,10 @@ ], "id": "P5071", "is_paper": true, - "keywords": [], + "keywords": [ + "human behavior analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.577.pdf", "paper_type": "Main-Oral", @@ -67398,7 +72448,10 @@ ], "id": "P5079", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.398.pdf", "paper_type": "findings", @@ -67433,7 +72486,10 @@ ], "id": "P5090", "is_paper": true, - "keywords": [], + "keywords": [ + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.161.pdf", "paper_type": "Main-Poster", @@ -67465,7 +72521,10 @@ ], "id": "P5095", "is_paper": true, - "keywords": [], + "keywords": [ + "mutlilingual representations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.854.pdf", "paper_type": "findings", @@ -67498,7 +72557,13 @@ ], "id": "P5097", "is_paper": true, - "keywords": [], + "keywords": [ + "reading comprehension", + "logical reasoning", + "generalization", + "reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.513.pdf", "paper_type": "findings", @@ -67532,7 +72597,10 @@ ], "id": "P5099", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodal applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.88.pdf", "paper_type": "Main-Poster", @@ -67569,7 +72637,10 @@ ], "id": "P51", "is_paper": true, - "keywords": [], + "keywords": [ + "financial/business nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.64.pdf", "paper_type": "Main-Poster", @@ -67604,7 +72675,10 @@ ], "id": "P5106", "is_paper": true, - "keywords": [], + "keywords": [ + "domain adaptation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.10.pdf", "paper_type": "Main-Poster", @@ -67638,7 +72712,10 @@ ], "id": "P5109", "is_paper": true, - "keywords": [], + "keywords": [ + "transfer learning / domain adaptation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.904.pdf", "paper_type": "Main-Poster", @@ -67675,7 +72752,10 @@ ], "id": "P511", "is_paper": true, - "keywords": [], + "keywords": [ + "inference methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.687.pdf", "paper_type": "Main-Poster", @@ -67710,6 +72790,7 @@ "id": "P5112", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.529.pdf", "paper_type": "Main-Poster", @@ -67744,7 +72825,10 @@ ], "id": "P5113", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.156.pdf", "paper_type": "Main-Oral", @@ -67777,7 +72861,12 @@ ], "id": "P5125", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation" + ], + "languages": [ + "sign language" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.665.pdf", "paper_type": "findings", @@ -67808,7 +72897,10 @@ ], "id": "P5135", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.876.pdf", "paper_type": "findings", @@ -67840,7 +72932,10 @@ ], "id": "P514", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.721.pdf", "paper_type": "Main-Poster", @@ -67871,7 +72966,10 @@ ], "id": "P5140", "is_paper": true, - "keywords": [], + "keywords": [ + "dependency parsing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.482.pdf", "paper_type": "findings", @@ -67911,7 +73009,10 @@ ], "id": "P5141", "is_paper": true, - "keywords": [], + "keywords": [ + "continual learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.612.pdf", "paper_type": "Main-Poster", @@ -67948,7 +73049,10 @@ ], "id": "P5150", "is_paper": true, - "keywords": [], + "keywords": [ + "reading comprehension" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.743.pdf", "paper_type": "findings", @@ -67979,7 +73083,10 @@ ], "id": "P5151", "is_paper": true, - "keywords": [], + "keywords": [ + "lessons from other fields" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.136.pdf", "paper_type": "Main-Poster", @@ -68015,7 +73122,10 @@ ], "id": "P5166", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.396.pdf", "paper_type": "findings", @@ -68052,7 +73162,10 @@ ], "id": "P5167", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.138.pdf", "paper_type": "Main-Poster", @@ -68085,6 +73198,7 @@ "id": "P5168", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.349.pdf", "paper_type": "findings", @@ -68116,7 +73230,10 @@ ], "id": "P5171", "is_paper": true, - "keywords": [], + "keywords": [ + "argument generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.425.pdf", "paper_type": "Main-Poster", @@ -68150,7 +73267,10 @@ ], "id": "P518", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.200.pdf", "paper_type": "Main-Poster", @@ -68187,7 +73307,10 @@ ], "id": "P5188", "is_paper": true, - "keywords": [], + "keywords": [ + "financial/business nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.219.pdf", "paper_type": "findings", @@ -68221,7 +73344,10 @@ ], "id": "P5193", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.363.pdf", "paper_type": "findings", @@ -68256,7 +73382,10 @@ ], "id": "P52", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge graphs" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.260.pdf", "paper_type": "findings", @@ -68291,7 +73420,11 @@ ], "id": "P5204", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation", + "online adaptation for mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.47.pdf", "paper_type": "Main-Oral", @@ -68324,7 +73457,10 @@ ], "id": "P5206", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.634.pdf", "paper_type": "Main-Poster", @@ -68361,7 +73497,10 @@ ], "id": "P521", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.236.pdf", "paper_type": "Main-Poster", @@ -68398,7 +73537,10 @@ ], "id": "P522", "is_paper": true, - "keywords": [], + "keywords": [ + "hate speech detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.66.pdf", "paper_type": "Main-Poster", @@ -68431,7 +73573,12 @@ ], "id": "P5234", "is_paper": true, - "keywords": [], + "keywords": [ + "factuality", + "knowledge augmented", + "grounded dialog" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.250.pdf", "paper_type": "Main-Poster", @@ -68463,7 +73610,10 @@ ], "id": "P5238", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.314.pdf", "paper_type": "Main-Poster", @@ -68498,7 +73648,10 @@ ], "id": "P5243", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.234.pdf", "paper_type": "Main-Poster", @@ -68533,7 +73686,10 @@ ], "id": "P5246", "is_paper": true, - "keywords": [], + "keywords": [ + "grounded dialog" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.148.pdf", "paper_type": "Main-Poster", @@ -68566,7 +73722,10 @@ ], "id": "P5254", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.553.pdf", "paper_type": "Main-Poster", @@ -68597,7 +73756,10 @@ ], "id": "P5255", "is_paper": true, - "keywords": [], + "keywords": [ + "reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.295.pdf", "paper_type": "findings", @@ -68629,7 +73791,10 @@ ], "id": "P5256", "is_paper": true, - "keywords": [], + "keywords": [ + "deep syntax parsing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.15.pdf", "paper_type": "Main-Oral", @@ -68665,7 +73830,10 @@ ], "id": "P526", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge base construction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.514.pdf", "paper_type": "Main-Poster", @@ -68699,7 +73867,10 @@ ], "id": "P5261", "is_paper": true, - "keywords": [], + "keywords": [ + "discourse relations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.292.pdf", "paper_type": "Main-Poster", @@ -68737,7 +73908,10 @@ ], "id": "P527", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.445.pdf", "paper_type": "Main-Poster", @@ -68771,7 +73945,16 @@ ], "id": "P5272", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual corpora" + ], + "languages": [ + "italian", + "french", + "german", + "polish", + "russian" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.169.pdf", "paper_type": "Main-Poster", @@ -68805,7 +73988,10 @@ ], "id": "P5289", "is_paper": true, - "keywords": [], + "keywords": [ + "probing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.276.pdf", "paper_type": "findings", @@ -68839,7 +74025,10 @@ ], "id": "P5293", "is_paper": true, - "keywords": [], + "keywords": [ + "dialogue state tracking" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.159.pdf", "paper_type": "Main-Poster", @@ -68872,7 +74061,10 @@ ], "id": "P5297", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.32.pdf", "paper_type": "Main-Poster", @@ -68904,7 +74096,10 @@ ], "id": "P530", "is_paper": true, - "keywords": [], + "keywords": [ + "fact checking, rumour/misinformation detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.311.pdf", "paper_type": "Main-Poster", @@ -68941,7 +74136,12 @@ ], "id": "P5312", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "scaling", + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.767.pdf", "paper_type": "Main-Poster", @@ -68977,7 +74177,10 @@ ], "id": "P5316", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.291.pdf", "paper_type": "Main-Poster", @@ -69012,7 +74215,10 @@ ], "id": "P532", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.819.pdf", "paper_type": "Main-Oral", @@ -69048,7 +74254,10 @@ ], "id": "P5325", "is_paper": true, - "keywords": [], + "keywords": [ + "textual entailment" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.56.pdf", "paper_type": "findings", @@ -69079,7 +74288,10 @@ ], "id": "P5335", "is_paper": true, - "keywords": [], + "keywords": [ + "image text matching" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.110.pdf", "paper_type": "Main-Poster", @@ -69112,7 +74324,10 @@ ], "id": "P5345", "is_paper": true, - "keywords": [], + "keywords": [ + "open-domain qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.401.pdf", "paper_type": "findings", @@ -69144,7 +74359,11 @@ ], "id": "P5352", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented", + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.685.pdf", "paper_type": "Main-Poster", @@ -69176,7 +74395,14 @@ ], "id": "P5357", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingualism" + ], + "languages": [ + "hindi", + "marathi", + "bengali" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.514.pdf", "paper_type": "findings", @@ -69209,7 +74435,13 @@ ], "id": "P536", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [ + "korean", + "hanja" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.180.pdf", "paper_type": "Main-Poster", @@ -69241,7 +74473,10 @@ ], "id": "P5362", "is_paper": true, - "keywords": [], + "keywords": [ + "parsing algorighms (symbolic, theoritical results)" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.420.pdf", "paper_type": "Main-Poster", @@ -69275,7 +74510,13 @@ ], "id": "P5374", "is_paper": true, - "keywords": [], + "keywords": [ + "frame detection and analysis", + "nlp tools for social analysis" + ], + "languages": [ + "italian" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.501.pdf", "paper_type": "findings", @@ -69306,7 +74547,10 @@ ], "id": "P5382", "is_paper": true, - "keywords": [], + "keywords": [ + "explanation faithfulness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.554.pdf", "paper_type": "findings", @@ -69338,7 +74582,10 @@ ], "id": "P5383", "is_paper": true, - "keywords": [], + "keywords": [ + "(non-)generalizability" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.828.pdf", "paper_type": "Main-Poster", @@ -69371,7 +74618,10 @@ ], "id": "P5388", "is_paper": true, - "keywords": [], + "keywords": [ + "representation learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.84.pdf", "paper_type": "Main-Poster", @@ -69402,7 +74652,10 @@ ], "id": "P5397", "is_paper": true, - "keywords": [], + "keywords": [ + "(non-)generalizability" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.127.pdf", "paper_type": "Main-Poster", @@ -69436,7 +74689,12 @@ ], "id": "P540", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodal applications" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.421.pdf", "paper_type": "Main-Poster", @@ -69469,7 +74727,10 @@ ], "id": "P5404", "is_paper": true, - "keywords": [], + "keywords": [ + "spoken language translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.115.pdf", "paper_type": "Main-Poster", @@ -69508,7 +74769,10 @@ ], "id": "P541", "is_paper": true, - "keywords": [], + "keywords": [ + "financial/business nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.633.pdf", "paper_type": "findings", @@ -69544,7 +74808,10 @@ ], "id": "P544", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.303.pdf", "paper_type": "Main-Poster", @@ -69580,7 +74847,11 @@ ], "id": "P5447", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "language resources" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.665.pdf", "paper_type": "Main-Poster", @@ -69612,6 +74883,7 @@ "id": "P5464", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.105.pdf", "paper_type": "findings", @@ -69649,7 +74921,11 @@ ], "id": "P547", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.256.pdf", "paper_type": "Main-Oral", @@ -69685,7 +74961,12 @@ ], "id": "P548", "is_paper": true, - "keywords": [], + "keywords": [ + "spoken language understanding", + "speech and vision", + "speech technologies" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.437.pdf", "paper_type": "findings", @@ -69717,7 +74998,10 @@ ], "id": "P5482", "is_paper": true, - "keywords": [], + "keywords": [ + "reading comprehension" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.537.pdf", "paper_type": "Main-Oral", @@ -69749,7 +75033,10 @@ ], "id": "P5486", "is_paper": true, - "keywords": [], + "keywords": [ + "fact checking, rumour/misinformation detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.387.pdf", "paper_type": "findings", @@ -69780,7 +75067,10 @@ ], "id": "P5489", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.7.pdf", "paper_type": "Main-Poster", @@ -69812,7 +75102,10 @@ ], "id": "P549", "is_paper": true, - "keywords": [], + "keywords": [ + "reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.588.pdf", "paper_type": "findings", @@ -69848,7 +75141,10 @@ ], "id": "P550", "is_paper": true, - "keywords": [], + "keywords": [ + "language resources" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.822.pdf", "paper_type": "Main-Poster", @@ -69883,7 +75179,13 @@ ], "id": "P553", "is_paper": true, - "keywords": [], + "keywords": [ + "spoken language understanding", + "speech and vision", + "speech technologies", + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.508.pdf", "paper_type": "findings", @@ -69923,7 +75225,13 @@ ], "id": "P555", "is_paper": true, - "keywords": [], + "keywords": [ + "spoken language translation", + "speech and vision", + "speech technologies", + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.479.pdf", "paper_type": "Main-Poster", @@ -69955,7 +75263,10 @@ ], "id": "P5561", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.59.pdf", "paper_type": "Main-Poster", @@ -69987,7 +75298,10 @@ ], "id": "P5563", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.530.pdf", "paper_type": "Main-Poster", @@ -70023,7 +75337,10 @@ ], "id": "P5565", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.793.pdf", "paper_type": "Main-Poster", @@ -70055,7 +75372,12 @@ ], "id": "P5566", "is_paper": true, - "keywords": [], + "keywords": [ + "chunking, shallow-parsing" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.356.pdf", "paper_type": "Main-Poster", @@ -70092,7 +75414,10 @@ ], "id": "P5567", "is_paper": true, - "keywords": [], + "keywords": [ + "commonsense qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.835.pdf", "paper_type": "findings", @@ -70126,7 +75451,10 @@ ], "id": "P5570", "is_paper": true, - "keywords": [], + "keywords": [ + "model compression methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.35.pdf", "paper_type": "Main-Oral", @@ -70158,7 +75486,10 @@ ], "id": "P5572", "is_paper": true, - "keywords": [], + "keywords": [ + "coherence" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.431.pdf", "paper_type": "Main-Poster", @@ -70190,7 +75521,10 @@ ], "id": "P5573", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.873.pdf", "paper_type": "Main-Poster", @@ -70224,7 +75558,10 @@ ], "id": "P5574", "is_paper": true, - "keywords": [], + "keywords": [ + "contrastive learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.585.pdf", "paper_type": "Main-Poster", @@ -70256,7 +75593,10 @@ ], "id": "P5575", "is_paper": true, - "keywords": [], + "keywords": [ + "representation learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.862.pdf", "paper_type": "Main-Poster", @@ -70289,7 +75629,10 @@ ], "id": "P5576", "is_paper": true, - "keywords": [], + "keywords": [ + "argument generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.786.pdf", "paper_type": "Main-Poster", @@ -70324,7 +75667,10 @@ ], "id": "P5577", "is_paper": true, - "keywords": [], + "keywords": [ + "probing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.494.pdf", "paper_type": "findings", @@ -70359,7 +75705,10 @@ ], "id": "P5578", "is_paper": true, - "keywords": [], + "keywords": [ + "computational psycholinguistics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.566.pdf", "paper_type": "Main-Poster", @@ -70396,7 +75745,10 @@ ], "id": "P5579", "is_paper": true, - "keywords": [], + "keywords": [ + "phrase/sentence embedding" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.197.pdf", "paper_type": "Main-Poster", @@ -70432,7 +75784,12 @@ ], "id": "P558", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.28.pdf", "paper_type": "Main-Poster", @@ -70463,7 +75820,10 @@ ], "id": "P5580", "is_paper": true, - "keywords": [], + "keywords": [ + "mathematical nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.205.pdf", "paper_type": "Main-Poster", @@ -70494,7 +75854,10 @@ ], "id": "P5582", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.92.pdf", "paper_type": "Main-Poster", @@ -70527,7 +75890,10 @@ ], "id": "P5583", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/unfairness mitigation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.108.pdf", "paper_type": "Main-Poster", @@ -70561,7 +75927,10 @@ ], "id": "P5589", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.623.pdf", "paper_type": "Main-Poster", @@ -70597,7 +75966,10 @@ ], "id": "P5591", "is_paper": true, - "keywords": [], + "keywords": [ + "table qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.449.pdf", "paper_type": "Main-Poster", @@ -70633,7 +76005,11 @@ ], "id": "P5596", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation", + "multilingual summarisation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.843.pdf", "paper_type": "Main-Poster", @@ -70670,7 +76046,10 @@ ], "id": "P5597", "is_paper": true, - "keywords": [], + "keywords": [ + "generalization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.161.pdf", "paper_type": "Main-Poster", @@ -70706,7 +76085,13 @@ ], "id": "P5599", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "benchmarking", + "nlp datasets", + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.416.pdf", "paper_type": "Main-Poster", @@ -70745,7 +76130,10 @@ ], "id": "P5600", "is_paper": true, - "keywords": [], + "keywords": [ + "spoken language translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.899.pdf", "paper_type": "Main-Poster", @@ -70782,7 +76170,10 @@ ], "id": "P5601", "is_paper": true, - "keywords": [], + "keywords": [ + "spoken language translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.602.pdf", "paper_type": "Main-Poster", @@ -70818,7 +76209,10 @@ ], "id": "P5603", "is_paper": true, - "keywords": [], + "keywords": [ + "data augmentation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.390.pdf", "paper_type": "Main-Poster", @@ -70849,7 +76243,10 @@ ], "id": "P5607", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual pre-training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.49.pdf", "paper_type": "Main-Poster", @@ -70882,7 +76279,10 @@ ], "id": "P5609", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.178.pdf", "paper_type": "Main-Poster", @@ -70916,7 +76316,12 @@ ], "id": "P5611", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-task learning", + "generalization", + "few-shot learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.589.pdf", "paper_type": "Main-Poster", @@ -70950,7 +76355,11 @@ ], "id": "P5614", "is_paper": true, - "keywords": [], + "keywords": [ + "analysis", + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.262.pdf", "paper_type": "Main-Oral", @@ -70984,7 +76393,10 @@ ], "id": "P5616", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.503.pdf", "paper_type": "Main-Poster", @@ -71019,7 +76431,10 @@ ], "id": "P5617", "is_paper": true, - "keywords": [], + "keywords": [ + "explanation faithfulness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.358.pdf", "paper_type": "Main-Poster", @@ -71054,7 +76469,10 @@ ], "id": "P5618", "is_paper": true, - "keywords": [], + "keywords": [ + "parameter-efficient finetuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.456.pdf", "paper_type": "Main-Poster", @@ -71086,7 +76504,10 @@ ], "id": "P5619", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.20.pdf", "paper_type": "Main-Poster", @@ -71121,7 +76542,10 @@ ], "id": "P5620", "is_paper": true, - "keywords": [], + "keywords": [ + "benchmarking" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.276.pdf", "paper_type": "Main-Poster", @@ -71157,7 +76581,11 @@ ], "id": "P5623", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation and metrics", + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.603.pdf", "paper_type": "Main-Poster", @@ -71190,7 +76618,10 @@ ], "id": "P5624", "is_paper": true, - "keywords": [], + "keywords": [ + "parameter-efficient finetuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.659.pdf", "paper_type": "Main-Poster", @@ -71223,7 +76654,10 @@ ], "id": "P5625", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.126.pdf", "paper_type": "findings", @@ -71255,7 +76689,10 @@ ], "id": "P5626", "is_paper": true, - "keywords": [], + "keywords": [ + "ethical considerations in nlp applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.709.pdf", "paper_type": "Main-Oral", @@ -71288,7 +76725,10 @@ ], "id": "P5627", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge tracing/discovering/inducing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.647.pdf", "paper_type": "findings", @@ -71321,7 +76761,11 @@ ], "id": "P563", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets", + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.255.pdf", "paper_type": "Main-Poster", @@ -71352,7 +76796,10 @@ ], "id": "P5631", "is_paper": true, - "keywords": [], + "keywords": [ + "natural language inference" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.252.pdf", "paper_type": "findings", @@ -71385,7 +76832,14 @@ ], "id": "P5632", "is_paper": true, - "keywords": [], + "keywords": [ + "language resources", + "nlp datasets", + "evaluation" + ], + "languages": [ + "french" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.393.pdf", "paper_type": "Main-Poster", @@ -71420,7 +76874,12 @@ ], "id": "P5635", "is_paper": true, - "keywords": [], + "keywords": [ + "passage retrieval", + "dense retrieval", + "contrastive learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.95.pdf", "paper_type": "Main-Poster", @@ -71457,7 +76916,11 @@ ], "id": "P5637", "is_paper": true, - "keywords": [], + "keywords": [ + "hate speech detection", + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.28.pdf", "paper_type": "Main-Poster", @@ -71488,7 +76951,10 @@ ], "id": "P5638", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation methodologies" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.437.pdf", "paper_type": "Main-Poster", @@ -71522,7 +76988,10 @@ ], "id": "P5639", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation and metrics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.241.pdf", "paper_type": "Main-Poster", @@ -71556,7 +77025,10 @@ ], "id": "P5640", "is_paper": true, - "keywords": [], + "keywords": [ + "reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.170.pdf", "paper_type": "Main-Poster", @@ -71591,7 +77063,11 @@ ], "id": "P5642", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot learning", + "human-in-the-loop / active learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.141.pdf", "paper_type": "Main-Poster", @@ -71626,7 +77102,10 @@ ], "id": "P5643", "is_paper": true, - "keywords": [], + "keywords": [ + "reading comprehension" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.559.pdf", "paper_type": "Main-Poster", @@ -71657,7 +77136,10 @@ ], "id": "P5644", "is_paper": true, - "keywords": [], + "keywords": [ + "reproducibility" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.633.pdf", "paper_type": "Main-Poster", @@ -71689,7 +77171,10 @@ ], "id": "P5646", "is_paper": true, - "keywords": [], + "keywords": [ + "linguistic theories" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.91.pdf", "paper_type": "Main-Poster", @@ -71724,7 +77209,10 @@ ], "id": "P5647", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.729.pdf", "paper_type": "Main-Poster", @@ -71759,7 +77247,13 @@ ], "id": "P5648", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "language resources", + "nlp datasets", + "datasets for low resource languages" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.415.pdf", "paper_type": "Main-Poster", @@ -71795,7 +77289,10 @@ ], "id": "P5650", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational summarization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.798.pdf", "paper_type": "Main-Poster", @@ -71830,6 +77327,9 @@ "id": "P5651", "is_paper": true, "keywords": [], + "languages": [ + "german" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.638.pdf", "paper_type": "Main-Poster", @@ -71868,7 +77368,10 @@ ], "id": "P5652", "is_paper": true, - "keywords": [], + "keywords": [ + "code generation and understanding" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.465.pdf", "paper_type": "Main-Poster", @@ -71899,7 +77402,10 @@ ], "id": "P5653", "is_paper": true, - "keywords": [], + "keywords": [ + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.342.pdf", "paper_type": "Main-Poster", @@ -71936,7 +77442,10 @@ ], "id": "P5655", "is_paper": true, - "keywords": [], + "keywords": [ + "modelling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.107.pdf", "paper_type": "findings", @@ -71970,7 +77479,11 @@ ], "id": "P5657", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot learning", + "meta learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.617.pdf", "paper_type": "Main-Poster", @@ -72003,7 +77516,10 @@ ], "id": "P5658", "is_paper": true, - "keywords": [], + "keywords": [ + "cognitive modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.415.pdf", "paper_type": "findings", @@ -72035,7 +77551,11 @@ ], "id": "P5660", "is_paper": true, - "keywords": [], + "keywords": [ + "explanation faithfulness", + "hardness of samples" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.582.pdf", "paper_type": "findings", @@ -72067,7 +77587,10 @@ ], "id": "P5661", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.277.pdf", "paper_type": "Main-Poster", @@ -72098,7 +77621,10 @@ ], "id": "P5662", "is_paper": true, - "keywords": [], + "keywords": [ + "reading comprehension" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.72.pdf", "paper_type": "Main-Poster", @@ -72132,7 +77658,10 @@ ], "id": "P5666", "is_paper": true, - "keywords": [], + "keywords": [ + "probing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.213.pdf", "paper_type": "findings", @@ -72166,7 +77695,10 @@ ], "id": "P5667", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial attacks/examples/training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.561.pdf", "paper_type": "findings", @@ -72209,7 +77741,12 @@ ], "id": "P5668", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic speech recognition", + "spoken language understanding", + "qa via spoken queries" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.639.pdf", "paper_type": "Main-Poster", @@ -72246,7 +77783,11 @@ ], "id": "P5669", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction", + "document-level extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.584.pdf", "paper_type": "Main-Poster", @@ -72279,6 +77820,7 @@ "id": "P5670", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.567.pdf", "paper_type": "findings", @@ -72310,7 +77852,13 @@ ], "id": "P5673", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis" + ], + "languages": [ + "french", + "dutch" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.574.pdf", "paper_type": "Main-Poster", @@ -72344,7 +77892,10 @@ ], "id": "P5676", "is_paper": true, - "keywords": [], + "keywords": [ + "scaling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.134.pdf", "paper_type": "Main-Poster", @@ -72377,7 +77928,10 @@ ], "id": "P5677", "is_paper": true, - "keywords": [], + "keywords": [ + "legal nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.481.pdf", "paper_type": "Main-Poster", @@ -72411,7 +77965,11 @@ ], "id": "P5679", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic creation and evaluation of language resources", + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.102.pdf", "paper_type": "Main-Poster", @@ -72447,7 +78005,10 @@ ], "id": "P568", "is_paper": true, - "keywords": [], + "keywords": [ + "zero/few-shot extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.545.pdf", "paper_type": "Main-Poster", @@ -72483,7 +78044,10 @@ ], "id": "P5680", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.25.pdf", "paper_type": "Main-Poster", @@ -72514,7 +78078,14 @@ ], "id": "P5682", "is_paper": true, - "keywords": [], + "keywords": [ + "linguistic theories", + "cognitive modeling", + "computational psycholinguistics" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.712.pdf", "paper_type": "Main-Poster", @@ -72546,7 +78117,10 @@ ], "id": "P5683", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge base qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.637.pdf", "paper_type": "Main-Poster", @@ -72577,7 +78151,13 @@ ], "id": "P5684", "is_paper": true, - "keywords": [], + "keywords": [ + "polysemy", + "paraphrasing", + "metaphor", + "cognition" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.184.pdf", "paper_type": "Main-Poster", @@ -72609,7 +78189,12 @@ ], "id": "P5686", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingualism", + "cross-lingual transfer", + "mutlilingual representations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.217.pdf", "paper_type": "Main-Poster", @@ -72641,7 +78226,10 @@ ], "id": "P5688", "is_paper": true, - "keywords": [], + "keywords": [ + "representation learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.717.pdf", "paper_type": "Main-Poster", @@ -72674,7 +78262,10 @@ ], "id": "P5691", "is_paper": true, - "keywords": [], + "keywords": [ + "generalization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.667.pdf", "paper_type": "Main-Poster", @@ -72708,7 +78299,10 @@ ], "id": "P5692", "is_paper": true, - "keywords": [], + "keywords": [ + "dense retrieval" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.746.pdf", "paper_type": "Main-Poster", @@ -72743,7 +78337,13 @@ ], "id": "P5693", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training", + "interpretability/analysis", + "continual learning", + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.275.pdf", "paper_type": "Main-Oral", @@ -72781,7 +78381,12 @@ ], "id": "P5694", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "document-level extraction", + "zero/few-shot extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.255.pdf", "paper_type": "findings", @@ -72811,7 +78416,10 @@ ], "id": "P5698", "is_paper": true, - "keywords": [], + "keywords": [ + "linguistic theories" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.302.pdf", "paper_type": "findings", @@ -72842,7 +78450,10 @@ ], "id": "P5699", "is_paper": true, - "keywords": [], + "keywords": [ + "language/cultural bias analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.26.pdf", "paper_type": "Main-Poster", @@ -72879,7 +78490,10 @@ ], "id": "P57", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge base qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.57.pdf", "paper_type": "Main-Poster", @@ -72913,7 +78527,10 @@ ], "id": "P5701", "is_paper": true, - "keywords": [], + "keywords": [ + "data augmentation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.484.pdf", "paper_type": "Main-Poster", @@ -72946,7 +78563,10 @@ ], "id": "P5706", "is_paper": true, - "keywords": [], + "keywords": [ + "meta learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.12.pdf", "paper_type": "Main-Poster", @@ -72979,7 +78599,10 @@ ], "id": "P5707", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.628.pdf", "paper_type": "Main-Poster", @@ -73014,7 +78637,12 @@ ], "id": "P5708", "is_paper": true, - "keywords": [], + "keywords": [ + "human-in-the-loop", + "bias/toxicity", + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.493.pdf", "paper_type": "Main-Poster", @@ -73051,7 +78679,11 @@ ], "id": "P571", "is_paper": true, - "keywords": [], + "keywords": [ + "scaling", + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.472.pdf", "paper_type": "findings", @@ -73088,7 +78720,10 @@ ], "id": "P5713", "is_paper": true, - "keywords": [], + "keywords": [ + "fact checking, rumour/misinformation detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.575.pdf", "paper_type": "Main-Poster", @@ -73123,7 +78758,12 @@ ], "id": "P5721", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.460.pdf", "paper_type": "Main-Poster", @@ -73156,7 +78796,10 @@ ], "id": "P5722", "is_paper": true, - "keywords": [], + "keywords": [ + "commonsense qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.831.pdf", "paper_type": "Main-Oral", @@ -73190,7 +78833,10 @@ ], "id": "P5723", "is_paper": true, - "keywords": [], + "keywords": [ + "security/privacy" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.155.pdf", "paper_type": "Main-Poster", @@ -73230,7 +78876,10 @@ ], "id": "P5724", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge-augmented methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.594.pdf", "paper_type": "Main-Poster", @@ -73265,7 +78914,12 @@ ], "id": "P5725", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic speech recognition", + "spoken language understanding", + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.363.pdf", "paper_type": "Main-Oral", @@ -73300,7 +78954,63 @@ ], "id": "P5726", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "benchmarking", + "language resources", + "multilingual corpora", + "automatic creation and evaluation of language resources", + "nlp datasets", + "automatic evaluation of datasets", + "evaluation methodologies", + "evaluation", + "datasets for low resource languages", + "metrics" + ], + "languages": [ + "amharic", + "arabic", + "azerbaijani", + "bengali", + "burmese", + "chinese", + "french", + "gujarati", + "hausa", + "hindi", + "igbo", + "indonesian", + "japanese", + "kirundi", + "korean", + "kyrgyz", + "marathi", + "nepali", + "oromo", + "pashto", + "persian", + "pcm", + "portuguese", + "panjabi", + "russian", + "scottish gaelic", + "serbian", + "sinhala", + "somali", + "spanish", + "swahili", + "tamil", + "telugu", + "thai", + "tigrinya", + "turkish", + "ukrainian", + "urdu", + "uzbek", + "vietnamese", + "welsh", + "yoruba" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.143.pdf", "paper_type": "Main-Poster", @@ -73333,7 +79043,10 @@ ], "id": "P5728", "is_paper": true, - "keywords": [], + "keywords": [ + "metaphor" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.9.pdf", "paper_type": "Main-Poster", @@ -73365,7 +79078,10 @@ ], "id": "P5730", "is_paper": true, - "keywords": [], + "keywords": [ + "ethical considerations in nlp applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.565.pdf", "paper_type": "Main-Poster", @@ -73396,7 +79112,13 @@ ], "id": "P5738", "is_paper": true, - "keywords": [], + "keywords": [ + "vision language navigation", + "cross-modal pretraining", + "cross-modal content generation", + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.737.pdf", "paper_type": "Main-Oral", @@ -73429,7 +79151,12 @@ ], "id": "P5739", "is_paper": true, - "keywords": [], + "keywords": [ + "linguistic theories", + "cognitive modeling", + "computational psycholinguistics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.773.pdf", "paper_type": "findings", @@ -73465,7 +79192,10 @@ ], "id": "P5740", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.789.pdf", "paper_type": "Main-Poster", @@ -73496,7 +79226,10 @@ ], "id": "P5741", "is_paper": true, - "keywords": [], + "keywords": [ + "ethical considerations in nlp applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.874.pdf", "paper_type": "findings", @@ -73527,7 +79260,10 @@ ], "id": "P5742", "is_paper": true, - "keywords": [], + "keywords": [ + "generalization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.801.pdf", "paper_type": "Main-Poster", @@ -73559,7 +79295,10 @@ ], "id": "P5743", "is_paper": true, - "keywords": [], + "keywords": [ + "open-domain qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.40.pdf", "paper_type": "Main-Poster", @@ -73602,7 +79341,10 @@ ], "id": "P5744", "is_paper": true, - "keywords": [], + "keywords": [ + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.773.pdf", "paper_type": "Main-Poster", @@ -73639,7 +79381,10 @@ ], "id": "P5745", "is_paper": true, - "keywords": [], + "keywords": [ + "dense retrieval" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.125.pdf", "paper_type": "Main-Poster", @@ -73674,7 +79419,10 @@ ], "id": "P5748", "is_paper": true, - "keywords": [], + "keywords": [ + "continual learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.703.pdf", "paper_type": "Main-Poster", @@ -73707,7 +79455,10 @@ ], "id": "P5749", "is_paper": true, - "keywords": [], + "keywords": [ + "free-text/natural language explanations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.108.pdf", "paper_type": "Main-Poster", @@ -73740,7 +79491,10 @@ ], "id": "P5755", "is_paper": true, - "keywords": [], + "keywords": [ + "hate-speech detection" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.21.pdf", "paper_type": "Main-Poster", @@ -73780,7 +79534,10 @@ ], "id": "P5756", "is_paper": true, - "keywords": [], + "keywords": [ + "grounded dialog" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.351.pdf", "paper_type": "findings", @@ -73814,7 +79571,10 @@ ], "id": "P5758", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.122.pdf", "paper_type": "findings", @@ -73846,7 +79606,10 @@ ], "id": "P576", "is_paper": true, - "keywords": [], + "keywords": [ + "textual entailment" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.196.pdf", "paper_type": "Main-Poster", @@ -73879,7 +79642,10 @@ ], "id": "P5760", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge graphs" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.591.pdf", "paper_type": "Main-Poster", @@ -73913,7 +79679,10 @@ ], "id": "P5766", "is_paper": true, - "keywords": [], + "keywords": [ + "human behavior analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.166.pdf", "paper_type": "Main-Poster", @@ -73946,7 +79715,10 @@ ], "id": "P5767", "is_paper": true, - "keywords": [], + "keywords": [ + "document-level extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.404.pdf", "paper_type": "Main-Poster", @@ -73979,7 +79751,10 @@ ], "id": "P5768", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.760.pdf", "paper_type": "Main-Poster", @@ -74013,7 +79788,10 @@ ], "id": "P5770", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.44.pdf", "paper_type": "Main-Poster", @@ -74046,7 +79824,10 @@ ], "id": "P5771", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge graphs" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.384.pdf", "paper_type": "Main-Poster", @@ -74084,7 +79865,11 @@ ], "id": "P5772", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation", + "factuality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.650.pdf", "paper_type": "Main-Poster", @@ -74117,7 +79902,10 @@ ], "id": "P5775", "is_paper": true, - "keywords": [], + "keywords": [ + "commonsense qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.90.pdf", "paper_type": "Main-Poster", @@ -74152,7 +79940,10 @@ ], "id": "P5777", "is_paper": true, - "keywords": [], + "keywords": [ + "transfer learning / domain adaptation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.92.pdf", "paper_type": "Main-Poster", @@ -74188,7 +79979,10 @@ ], "id": "P5784", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.611.pdf", "paper_type": "Main-Oral", @@ -74224,7 +80018,10 @@ ], "id": "P5785", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge graphs" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.391.pdf", "paper_type": "Main-Poster", @@ -74260,7 +80057,10 @@ ], "id": "P5790", "is_paper": true, - "keywords": [], + "keywords": [ + "open-domain qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.808.pdf", "paper_type": "Main-Poster", @@ -74293,7 +80093,10 @@ ], "id": "P5791", "is_paper": true, - "keywords": [], + "keywords": [ + "open information extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.640.pdf", "paper_type": "Main-Poster", @@ -74326,7 +80129,10 @@ ], "id": "P5792", "is_paper": true, - "keywords": [], + "keywords": [ + "efficient models" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.13.pdf", "paper_type": "Main-Poster", @@ -74358,7 +80164,10 @@ ], "id": "P5793", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge tracing/discovering/inducing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.95.pdf", "paper_type": "Main-Poster", @@ -74394,7 +80203,10 @@ ], "id": "P5796", "is_paper": true, - "keywords": [], + "keywords": [ + "representation learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.871.pdf", "paper_type": "Main-Poster", @@ -74434,7 +80246,11 @@ ], "id": "P5797", "is_paper": true, - "keywords": [], + "keywords": [ + "ai hype & expectations", + "lessons from other fields" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.903.pdf", "paper_type": "Main-Poster", @@ -74479,7 +80295,14 @@ ], "id": "P5798", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingualism", + "cross-lingual transfer", + "mutlilingual representations", + "multilingual benchmarks", + "multilingual evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.235.pdf", "paper_type": "Main-Oral", @@ -74514,7 +80337,10 @@ ], "id": "P5805", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.753.pdf", "paper_type": "Main-Poster", @@ -74548,7 +80374,10 @@ ], "id": "P5806", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.289.pdf", "paper_type": "Main-Poster", @@ -74584,7 +80413,11 @@ ], "id": "P5809", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "zero/few-shot extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.577.pdf", "paper_type": "findings", @@ -74617,7 +80450,10 @@ ], "id": "P581", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.123.pdf", "paper_type": "Main-Poster", @@ -74653,7 +80489,11 @@ ], "id": "P5810", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge-augmented methods", + "structured prediction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.422.pdf", "paper_type": "Main-Poster", @@ -74689,7 +80529,14 @@ ], "id": "P5811", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation", + "interactive mt", + "modelling" + ], + "languages": [ + "korean" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.298.pdf", "paper_type": "findings", @@ -74725,7 +80572,11 @@ ], "id": "P5812", "is_paper": true, - "keywords": [], + "keywords": [ + "probing", + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.35.pdf", "paper_type": "Main-Poster", @@ -74757,7 +80608,10 @@ ], "id": "P5815", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.234.pdf", "paper_type": "findings", @@ -74791,7 +80645,10 @@ ], "id": "P5816", "is_paper": true, - "keywords": [], + "keywords": [ + "reinforcement learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.57.pdf", "paper_type": "Main-Poster", @@ -74828,7 +80685,10 @@ ], "id": "P5818", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.223.pdf", "paper_type": "findings", @@ -74866,7 +80726,10 @@ ], "id": "P582", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal content generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.527.pdf", "paper_type": "Main-Poster", @@ -74902,7 +80765,10 @@ ], "id": "P5822", "is_paper": true, - "keywords": [], + "keywords": [ + "mutlilingual representations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.134.pdf", "paper_type": "findings", @@ -74935,7 +80801,10 @@ ], "id": "P5824", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge base construction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.23.pdf", "paper_type": "Main-Poster", @@ -74968,7 +80837,10 @@ ], "id": "P5826", "is_paper": true, - "keywords": [], + "keywords": [ + "cognitive modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.521.pdf", "paper_type": "Main-Poster", @@ -75003,7 +80875,10 @@ ], "id": "P5828", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.758.pdf", "paper_type": "Main-Poster", @@ -75035,7 +80910,11 @@ ], "id": "P5830", "is_paper": true, - "keywords": [], + "keywords": [ + "less-resourced languages", + "resources for less-resourced languages" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.696.pdf", "paper_type": "findings", @@ -75068,7 +80947,10 @@ ], "id": "P5831", "is_paper": true, - "keywords": [], + "keywords": [ + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.319.pdf", "paper_type": "findings", @@ -75102,7 +80984,12 @@ ], "id": "P5832", "is_paper": true, - "keywords": [], + "keywords": [ + "argument mining", + "argument quality assessment", + "argument schemes and reasoning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.338.pdf", "paper_type": "Main-Poster", @@ -75136,7 +81023,12 @@ ], "id": "P5833", "is_paper": true, - "keywords": [], + "keywords": [ + "multihop qa", + "few-shot qa", + "open-domain qa" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.885.pdf", "paper_type": "Main-Poster", @@ -75169,7 +81061,10 @@ ], "id": "P5836", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.337.pdf", "paper_type": "Main-Oral", @@ -75203,7 +81098,10 @@ ], "id": "P5837", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.313.pdf", "paper_type": "findings", @@ -75234,7 +81132,11 @@ ], "id": "P584", "is_paper": true, - "keywords": [], + "keywords": [ + "applications", + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.475.pdf", "paper_type": "Main-Poster", @@ -75266,7 +81168,12 @@ ], "id": "P5840", "is_paper": true, - "keywords": [], + "keywords": [ + "extractive summarisation", + "evaluation", + "factuality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.120.pdf", "paper_type": "Main-Poster", @@ -75300,7 +81207,10 @@ ], "id": "P5841", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial attacks/examples/training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.677.pdf", "paper_type": "findings", @@ -75333,7 +81243,10 @@ ], "id": "P5846", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.133.pdf", "paper_type": "Main-Oral", @@ -75367,7 +81280,10 @@ ], "id": "P5847", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.191.pdf", "paper_type": "findings", @@ -75399,7 +81315,12 @@ ], "id": "P5848", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial attacks/examples/training", + "data shortcuts/artifacts", + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.725.pdf", "paper_type": "Main-Poster", @@ -75434,7 +81355,11 @@ ], "id": "P585", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.226.pdf", "paper_type": "Main-Poster", @@ -75467,7 +81392,10 @@ ], "id": "P5850", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.214.pdf", "paper_type": "Main-Poster", @@ -75502,7 +81430,10 @@ ], "id": "P5854", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp tools for social analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.128.pdf", "paper_type": "Main-Poster", @@ -75535,7 +81466,14 @@ ], "id": "P5855", "is_paper": true, - "keywords": [], + "keywords": [ + "mathematical nlp" + ], + "languages": [ + "mathematica", + "latex", + "semantic latex" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.645.pdf", "paper_type": "Main-Poster", @@ -75568,7 +81506,10 @@ ], "id": "P5856", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.744.pdf", "paper_type": "findings", @@ -75599,7 +81540,11 @@ ], "id": "P5857", "is_paper": true, - "keywords": [], + "keywords": [ + "optimization methods", + "meta learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.104.pdf", "paper_type": "Main-Poster", @@ -75633,7 +81578,11 @@ ], "id": "P5859", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training", + "scaling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.146.pdf", "paper_type": "Main-Poster", @@ -75668,7 +81617,10 @@ ], "id": "P5869", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.455.pdf", "paper_type": "Main-Poster", @@ -75700,7 +81652,12 @@ ], "id": "P589", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.755.pdf", "paper_type": "Main-Poster", @@ -75735,7 +81692,10 @@ ], "id": "P590", "is_paper": true, - "keywords": [], + "keywords": [ + "pre-training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.669.pdf", "paper_type": "findings", @@ -75770,7 +81730,12 @@ ], "id": "P596", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.419.pdf", "paper_type": "Main-Poster", @@ -75804,7 +81769,10 @@ ], "id": "P598", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.24.pdf", "paper_type": "Main-Poster", @@ -75836,7 +81804,10 @@ ], "id": "P599", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.239.pdf", "paper_type": "Main-Poster", @@ -75867,7 +81838,10 @@ ], "id": "P600", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal pretraining" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.223.pdf", "paper_type": "Main-Poster", @@ -75906,7 +81880,10 @@ ], "id": "P601", "is_paper": true, - "keywords": [], + "keywords": [ + "security/privacy" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.423.pdf", "paper_type": "Main-Poster", @@ -75940,7 +81917,10 @@ ], "id": "P604", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge graphs" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.882.pdf", "paper_type": "findings", @@ -75973,7 +81953,10 @@ ], "id": "P605", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge graphs" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.743.pdf", "paper_type": "Main-Poster", @@ -76010,7 +81993,10 @@ ], "id": "P606", "is_paper": true, - "keywords": [], + "keywords": [ + "psycho-demographic trait prediction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.691.pdf", "paper_type": "findings", @@ -76044,7 +82030,10 @@ ], "id": "P608", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal pretraining" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.315.pdf", "paper_type": "Main-Poster", @@ -76078,7 +82067,10 @@ ], "id": "P609", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.823.pdf", "paper_type": "Main-Poster", @@ -76109,7 +82101,10 @@ ], "id": "P612", "is_paper": true, - "keywords": [], + "keywords": [ + "speech translation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.884.pdf", "paper_type": "Main-Poster", @@ -76143,7 +82138,10 @@ ], "id": "P614", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.91.pdf", "paper_type": "Main-Poster", @@ -76174,7 +82172,10 @@ ], "id": "P619", "is_paper": true, - "keywords": [], + "keywords": [ + "educational applications, gec, essay scoring" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.83.pdf", "paper_type": "Main-Oral", @@ -76205,7 +82206,10 @@ ], "id": "P620", "is_paper": true, - "keywords": [], + "keywords": [ + "model compression methods" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.114.pdf", "paper_type": "Main-Poster", @@ -76243,7 +82247,10 @@ ], "id": "P624", "is_paper": true, - "keywords": [], + "keywords": [ + "representation learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.771.pdf", "paper_type": "Main-Oral", @@ -76283,7 +82290,15 @@ ], "id": "P626", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "language resources", + "datasets for low resource languages" + ], + "languages": [ + "swiss german", + "german" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.150.pdf", "paper_type": "Main-Poster", @@ -76322,7 +82337,10 @@ ], "id": "P63", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodal applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.135.pdf", "paper_type": "Main-Poster", @@ -76354,7 +82372,10 @@ ], "id": "P64", "is_paper": true, - "keywords": [], + "keywords": [ + "document-level extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.354.pdf", "paper_type": "Main-Poster", @@ -76388,7 +82409,10 @@ ], "id": "P642", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.627.pdf", "paper_type": "Main-Poster", @@ -76419,7 +82443,10 @@ ], "id": "P645", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.668.pdf", "paper_type": "findings", @@ -76452,7 +82479,10 @@ ], "id": "P650", "is_paper": true, - "keywords": [], + "keywords": [ + "adversarial attacks/examples/training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.100.pdf", "paper_type": "Main-Poster", @@ -76485,7 +82515,10 @@ ], "id": "P651", "is_paper": true, - "keywords": [], + "keywords": [ + "argument mining" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.52.pdf", "paper_type": "Main-Poster", @@ -76523,7 +82556,10 @@ ], "id": "P652", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.811.pdf", "paper_type": "Main-Oral", @@ -76555,7 +82591,14 @@ ], "id": "P657", "is_paper": true, - "keywords": [], + "keywords": [ + "linguistic theories" + ], + "languages": [ + "tagalog", + "bikol", + "cebuano" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.331.pdf", "paper_type": "findings", @@ -76586,7 +82629,10 @@ ], "id": "P658", "is_paper": true, - "keywords": [], + "keywords": [ + "applications" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.672.pdf", "paper_type": "Main-Poster", @@ -76618,7 +82664,10 @@ ], "id": "P661", "is_paper": true, - "keywords": [], + "keywords": [ + "ai hype & expectations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.67.pdf", "paper_type": "findings", @@ -76653,7 +82702,12 @@ ], "id": "P663", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "scaling", + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.660.pdf", "paper_type": "Main-Poster", @@ -76686,7 +82740,10 @@ ], "id": "P664", "is_paper": true, - "keywords": [], + "keywords": [ + "probing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.839.pdf", "paper_type": "findings", @@ -76719,7 +82776,10 @@ ], "id": "P676", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge tracing/discovering/inducing" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.893.pdf", "paper_type": "Main-Poster", @@ -76751,7 +82811,10 @@ ], "id": "P677", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.138.pdf", "paper_type": "Main-Oral", @@ -76784,7 +82847,10 @@ ], "id": "P679", "is_paper": true, - "keywords": [], + "keywords": [ + "text-to-text generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.190.pdf", "paper_type": "Main-Poster", @@ -76815,7 +82881,10 @@ ], "id": "P68", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.83.pdf", "paper_type": "Main-Poster", @@ -76848,7 +82917,10 @@ ], "id": "P680", "is_paper": true, - "keywords": [], + "keywords": [ + "parameter-efficient finetuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.120.pdf", "paper_type": "Main-Poster", @@ -76880,7 +82952,10 @@ ], "id": "P683", "is_paper": true, - "keywords": [], + "keywords": [ + "financial/business nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.368.pdf", "paper_type": "Main-Poster", @@ -76910,7 +82985,12 @@ ], "id": "P692", "is_paper": true, - "keywords": [], + "keywords": [ + "(non-)reproducibility", + "evaluation", + "methodology" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.107.pdf", "paper_type": "Main-Poster", @@ -76946,7 +83026,14 @@ ], "id": "P693", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [ + "chinese", + "spanish", + "indonesian" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.417.pdf", "paper_type": "Main-Oral", @@ -76979,7 +83066,10 @@ ], "id": "P701", "is_paper": true, - "keywords": [], + "keywords": [ + "argument mining" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.802.pdf", "paper_type": "Main-Poster", @@ -77014,7 +83104,10 @@ ], "id": "P703", "is_paper": true, - "keywords": [], + "keywords": [ + "human-in-the-loop" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.224.pdf", "paper_type": "Main-Oral", @@ -77047,7 +83140,10 @@ ], "id": "P705", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge augmented" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.182.pdf", "paper_type": "findings", @@ -77082,7 +83178,12 @@ ], "id": "P707", "is_paper": true, - "keywords": [], + "keywords": [ + "corpus creation", + "language resources", + "nlp datasets" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.565.pdf", "paper_type": "findings", @@ -77117,7 +83218,10 @@ ], "id": "P712", "is_paper": true, - "keywords": [], + "keywords": [ + "passage retrieval" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.274.pdf", "paper_type": "Main-Oral", @@ -77150,7 +83254,11 @@ ], "id": "P713", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer", + "multilingual pre-training" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.131.pdf", "paper_type": "Main-Poster", @@ -77182,7 +83290,10 @@ ], "id": "P718", "is_paper": true, - "keywords": [], + "keywords": [ + "argument mining" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.86.pdf", "paper_type": "Main-Poster", @@ -77214,7 +83325,13 @@ ], "id": "P720", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingualism", + "text-to-text generation" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.462.pdf", "paper_type": "Main-Poster", @@ -77251,7 +83368,10 @@ ], "id": "P726", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.63.pdf", "paper_type": "findings", @@ -77285,7 +83405,10 @@ ], "id": "P728", "is_paper": true, - "keywords": [], + "keywords": [ + "data ethics" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.505.pdf", "paper_type": "Main-Oral", @@ -77322,7 +83445,10 @@ ], "id": "P736", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic creation and evaluation of language resources" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.419.pdf", "paper_type": "findings", @@ -77362,7 +83488,12 @@ ], "id": "P739", "is_paper": true, - "keywords": [], + "keywords": [ + "argument mining" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.849.pdf", "paper_type": "findings", @@ -77396,7 +83527,10 @@ ], "id": "P747", "is_paper": true, - "keywords": [], + "keywords": [ + "generalization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.351.pdf", "paper_type": "Main-Poster", @@ -77430,7 +83564,12 @@ ], "id": "P751", "is_paper": true, - "keywords": [], + "keywords": [ + "less-resourced languages" + ], + "languages": [ + "hebrew" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.487.pdf", "paper_type": "findings", @@ -77465,7 +83604,10 @@ ], "id": "P759", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.536.pdf", "paper_type": "Main-Poster", @@ -77501,7 +83643,10 @@ ], "id": "P76", "is_paper": true, - "keywords": [], + "keywords": [ + "few-shot summarisation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.718.pdf", "paper_type": "Main-Poster", @@ -77537,7 +83682,10 @@ ], "id": "P766", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.360.pdf", "paper_type": "Main-Poster", @@ -77571,7 +83719,11 @@ ], "id": "P773", "is_paper": true, - "keywords": [], + "keywords": [ + "grounded dialog", + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.457.pdf", "paper_type": "Main-Poster", @@ -77606,7 +83758,10 @@ ], "id": "P778", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation methodologies" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.33.pdf", "paper_type": "Main-Oral", @@ -77642,7 +83797,10 @@ ], "id": "P784", "is_paper": true, - "keywords": [], + "keywords": [ + "dense retrieval" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.734.pdf", "paper_type": "findings", @@ -77676,7 +83834,10 @@ ], "id": "P786", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodality" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.833.pdf", "paper_type": "Main-Oral", @@ -77711,7 +83872,10 @@ ], "id": "P788", "is_paper": true, - "keywords": [], + "keywords": [ + "code generation and understanding" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.678.pdf", "paper_type": "findings", @@ -77743,7 +83907,10 @@ ], "id": "P789", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.458.pdf", "paper_type": "Main-Poster", @@ -77774,7 +83941,10 @@ ], "id": "P790", "is_paper": true, - "keywords": [], + "keywords": [ + "topic modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.776.pdf", "paper_type": "Main-Poster", @@ -77806,7 +83976,10 @@ ], "id": "P792", "is_paper": true, - "keywords": [], + "keywords": [ + "coherence" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.463.pdf", "paper_type": "Main-Oral", @@ -77845,7 +84018,10 @@ ], "id": "P795", "is_paper": true, - "keywords": [], + "keywords": [ + "scaling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.35.pdf", "paper_type": "findings", @@ -77878,7 +84054,10 @@ ], "id": "P799", "is_paper": true, - "keywords": [], + "keywords": [ + "document-level extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.847.pdf", "paper_type": "Main-Poster", @@ -77910,7 +84089,24 @@ ], "id": "P803", "is_paper": true, - "keywords": [], + "keywords": [ + "named entity recognition and relation extraction", + "multilingual extraction" + ], + "languages": [ + "arabic", + "german", + "spanish; castilian", + "finnish", + "french", + "hindi", + "hungarian", + "japanese", + "polish", + "russian", + "turkish", + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.210.pdf", "paper_type": "Main-Oral", @@ -77948,7 +84144,10 @@ ], "id": "P807", "is_paper": true, - "keywords": [], + "keywords": [ + "domain adaptation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.321.pdf", "paper_type": "Main-Poster", @@ -77979,7 +84178,10 @@ ], "id": "P809", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.645.pdf", "paper_type": "findings", @@ -78013,7 +84215,10 @@ ], "id": "P810", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.320.pdf", "paper_type": "Main-Poster", @@ -78047,7 +84252,14 @@ ], "id": "P813", "is_paper": true, - "keywords": [], + "keywords": [ + "transfer learning / domain adaptation", + "parameter-efficient finetuning", + "few-shot learning" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.625.pdf", "paper_type": "Main-Poster", @@ -78081,7 +84293,10 @@ ], "id": "P819", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal content generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.451.pdf", "paper_type": "Main-Poster", @@ -78114,7 +84329,10 @@ ], "id": "P821", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.897.pdf", "paper_type": "Main-Poster", @@ -78146,7 +84364,10 @@ ], "id": "P823", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingualism" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.398.pdf", "paper_type": "Main-Poster", @@ -78179,7 +84400,10 @@ ], "id": "P824", "is_paper": true, - "keywords": [], + "keywords": [ + "open information extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.19.pdf", "paper_type": "Main-Poster", @@ -78212,7 +84436,12 @@ ], "id": "P832", "is_paper": true, - "keywords": [], + "keywords": [ + "automatic evaluation" + ], + "languages": [ + "german" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.3.pdf", "paper_type": "Main-Poster", @@ -78248,7 +84477,13 @@ ], "id": "P834", "is_paper": true, - "keywords": [], + "keywords": [ + "evaluation", + "methodology", + "ai hype & expectations", + "science-vs-engineering" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.477.pdf", "paper_type": "Main-Oral", @@ -78282,7 +84517,12 @@ ], "id": "P835", "is_paper": true, - "keywords": [], + "keywords": [ + "paraphrase recognition", + "textual entailment", + "natural language inference" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.182.pdf", "paper_type": "Main-Poster", @@ -78313,7 +84553,10 @@ ], "id": "P844", "is_paper": true, - "keywords": [], + "keywords": [ + "generative models" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.745.pdf", "paper_type": "findings", @@ -78350,7 +84593,10 @@ ], "id": "P845", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.538.pdf", "paper_type": "Main-Poster", @@ -78390,7 +84636,13 @@ ], "id": "P85", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual summarisation" + ], + "languages": [ + "mandarin", + "french" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.519.pdf", "paper_type": "Main-Poster", @@ -78428,7 +84680,11 @@ ], "id": "P854", "is_paper": true, - "keywords": [], + "keywords": [ + "contrastive learning", + "representation learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.728.pdf", "paper_type": "Main-Oral", @@ -78462,7 +84718,10 @@ ], "id": "P855", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodal summarization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.476.pdf", "paper_type": "Main-Poster", @@ -78499,7 +84758,12 @@ ], "id": "P860", "is_paper": true, - "keywords": [], + "keywords": [ + "datasets for low resource languages" + ], + "languages": [ + "korean" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.371.pdf", "paper_type": "Main-Poster", @@ -78535,7 +84799,15 @@ ], "id": "P867", "is_paper": true, - "keywords": [], + "keywords": [ + "speech technologies" + ], + "languages": [ + "hokkien", + "french", + "russian", + "spanish" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.504.pdf", "paper_type": "Main-Poster", @@ -78573,7 +84845,10 @@ ], "id": "P868", "is_paper": true, - "keywords": [], + "keywords": [ + "fine-tuning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.509.pdf", "paper_type": "Main-Oral", @@ -78609,7 +84884,10 @@ ], "id": "P872", "is_paper": true, - "keywords": [], + "keywords": [ + "question generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.30.pdf", "paper_type": "findings", @@ -78645,7 +84923,10 @@ ], "id": "P882", "is_paper": true, - "keywords": [], + "keywords": [ + "representation learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.570.pdf", "paper_type": "findings", @@ -78677,7 +84958,10 @@ ], "id": "P883", "is_paper": true, - "keywords": [], + "keywords": [ + "coreference resolution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.588.pdf", "paper_type": "Main-Oral", @@ -78711,7 +84995,10 @@ ], "id": "P886", "is_paper": true, - "keywords": [], + "keywords": [ + "discourse relations" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.325.pdf", "paper_type": "Main-Poster", @@ -78742,7 +85029,11 @@ ], "id": "P895", "is_paper": true, - "keywords": [], + "keywords": [ + "explanation faithfulness", + "feature attribution" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.261.pdf", "paper_type": "Main-Oral", @@ -78779,7 +85070,10 @@ ], "id": "P896", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-modal dialogue systems" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.405.pdf", "paper_type": "Main-Poster", @@ -78813,7 +85107,10 @@ ], "id": "P899", "is_paper": true, - "keywords": [], + "keywords": [ + "multilingual mt" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.883.pdf", "paper_type": "Main-Oral", @@ -78847,7 +85144,11 @@ ], "id": "P907", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal content generation", + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.510.pdf", "paper_type": "Main-Poster", @@ -78880,7 +85181,13 @@ ], "id": "P908", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal content generation", + "cross-modal application" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.146.pdf", "paper_type": "Main-Oral", @@ -78911,7 +85218,10 @@ ], "id": "P909", "is_paper": true, - "keywords": [], + "keywords": [ + "argument generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.466.pdf", "paper_type": "Main-Poster", @@ -78949,7 +85259,10 @@ ], "id": "P91", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.294.pdf", "paper_type": "Main-Poster", @@ -78987,7 +85300,10 @@ ], "id": "P910", "is_paper": true, - "keywords": [], + "keywords": [ + "semantic textual similarity" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.879.pdf", "paper_type": "Main-Poster", @@ -79022,7 +85338,10 @@ ], "id": "P915", "is_paper": true, - "keywords": [], + "keywords": [ + "interpretability/analysis" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.543.pdf", "paper_type": "Main-Poster", @@ -79055,7 +85374,16 @@ ], "id": "P921", "is_paper": true, - "keywords": [], + "keywords": [ + "less-resourced languages", + "resources for less-resourced languages" + ], + "languages": [ + "hausa", + "igbo", + "nigerian pidgin", + "yoruba" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.85.pdf", "paper_type": "Main-Oral", @@ -79092,7 +85420,10 @@ ], "id": "P923", "is_paper": true, - "keywords": [], + "keywords": [ + "passage retrieval" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.663.pdf", "paper_type": "Main-Oral", @@ -79127,7 +85458,10 @@ ], "id": "P927", "is_paper": true, - "keywords": [], + "keywords": [ + "topic modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.265.pdf", "paper_type": "Main-Poster", @@ -79159,7 +85493,10 @@ ], "id": "P933", "is_paper": true, - "keywords": [], + "keywords": [ + "multi-task learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.201.pdf", "paper_type": "Main-Poster", @@ -79193,7 +85530,10 @@ ], "id": "P938", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-lingual transfer" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.338.pdf", "paper_type": "findings", @@ -79232,7 +85572,10 @@ ], "id": "P941", "is_paper": true, - "keywords": [], + "keywords": [ + "representation learning" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.335.pdf", "paper_type": "findings", @@ -79266,7 +85609,10 @@ ], "id": "P942", "is_paper": true, - "keywords": [], + "keywords": [ + "model bias/fairness evaluation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.244.pdf", "paper_type": "Main-Poster", @@ -79298,7 +85644,13 @@ ], "id": "P953", "is_paper": true, - "keywords": [], + "keywords": [ + "task-oriented", + "knowledge augmented", + "commonsense reasoning", + "conversational modeling" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.417.pdf", "paper_type": "findings", @@ -79333,7 +85685,10 @@ ], "id": "P955", "is_paper": true, - "keywords": [], + "keywords": [ + "healthcare applications, clincial nlp" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.42.pdf", "paper_type": "Main-Poster", @@ -79364,7 +85719,11 @@ ], "id": "P957", "is_paper": true, - "keywords": [], + "keywords": [ + "generative models", + "graphical models" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.99.pdf", "paper_type": "Main-Poster", @@ -79397,7 +85756,12 @@ ], "id": "P958", "is_paper": true, - "keywords": [], + "keywords": [ + "knowledge-augmented methods", + "transfer learning / domain adaptation", + "generalization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.571.pdf", "paper_type": "findings", @@ -79431,7 +85795,15 @@ ], "id": "P959", "is_paper": true, - "keywords": [], + "keywords": [ + "human behavior analysis", + "stance detection", + "emotion detection and analysis", + "sociolinguistics", + "nlp tools for social analysis", + "quantiative analyses of news and/or social media" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.48.pdf", "paper_type": "Main-Poster", @@ -79462,7 +85834,10 @@ ], "id": "P965", "is_paper": true, - "keywords": [], + "keywords": [ + "textual entailment" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.554.pdf", "paper_type": "Main-Poster", @@ -79493,7 +85868,10 @@ ], "id": "P968", "is_paper": true, - "keywords": [], + "keywords": [ + "abstractive summarisation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-short.56.pdf", "paper_type": "Main-Oral", @@ -79526,7 +85904,10 @@ ], "id": "P970", "is_paper": true, - "keywords": [], + "keywords": [ + "query-focused summarization" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.713.pdf", "paper_type": "Main-Oral", @@ -79559,7 +85940,11 @@ ], "id": "P971", "is_paper": true, - "keywords": [], + "keywords": [ + "passage retrieval", + "dense retrieval" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.365.pdf", "paper_type": "Main-Oral", @@ -79593,7 +85978,12 @@ ], "id": "P976", "is_paper": true, - "keywords": [], + "keywords": [ + "nlp datasets" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.858.pdf", "paper_type": "Main-Poster", @@ -79625,7 +86015,10 @@ ], "id": "P982", "is_paper": true, - "keywords": [], + "keywords": [ + "event extraction" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.662.pdf", "paper_type": "findings", @@ -79658,7 +86051,10 @@ ], "id": "P985", "is_paper": true, - "keywords": [], + "keywords": [ + "question generation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.899.pdf", "paper_type": "findings", @@ -79694,7 +86090,10 @@ ], "id": "P986", "is_paper": true, - "keywords": [], + "keywords": [ + "document representation" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.findings-acl.358.pdf", "paper_type": "findings", @@ -79727,7 +86126,12 @@ ], "id": "P987", "is_paper": true, - "keywords": [], + "keywords": [ + "prompting", + "interpretability/analysis", + "robustness" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.68.pdf", "paper_type": "Main-Poster", @@ -79759,7 +86163,11 @@ ], "id": "P989", "is_paper": true, - "keywords": [], + "keywords": [ + "constituency parsing", + "grammar and knowledge-based approaches" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.316.pdf", "paper_type": "Main-Oral", @@ -79793,7 +86201,10 @@ ], "id": "P991", "is_paper": true, - "keywords": [], + "keywords": [ + "cross-modal application" + ], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.794.pdf", "paper_type": "Main-Poster", @@ -79826,7 +86237,12 @@ ], "id": "P998", "is_paper": true, - "keywords": [], + "keywords": [ + "multimodal applications" + ], + "languages": [ + "chinese" + ], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-long.27.pdf", "paper_type": "Main-Poster", @@ -79843,7 +86259,7 @@ "video_url": null }, "S113": { - "abstract": "We reveal that the bias parameters in the word prediction head of Transformer LMs play a significant role in the model's ability to reflect corpus word frequency.", + "abstract": "Prediction head is a crucial component of Transformer language models. Despite its direct impact on prediction, this component has often been overlooked in analyzing Transformers. In this study, we investigate the inner workings of the prediction head, specifically focusing on bias parameters. Our experiments with BERT and GPT-2 models reveal that the biases in their word prediction heads play a significant role in the models' ability to reflect word frequency in a corpus, aligning with the logit adjustment method commonly used in long-tailed learning. We also quantify the effect of controlling the biases in practical auto-regressive text generation scenarios; under a particular setting, more diverse text can be generated without compromising text quality.", "anthology_url": "https://aclanthology.org/2023.acl-srw.41", "authors": [ "Goro Kobayashi", @@ -79860,6 +86276,7 @@ "id": "S113", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.41.pdf", "paper_type": "srw", @@ -79869,14 +86286,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Transformer Language Models Handle Word Frequency in Prediction Head", - "tldr": "We reveal that the bias parameters in the word prediction head of Transformer LMs play a significant role in the model's ability to reflect corpus word frequency....", + "tldr": "Prediction head is a crucial component of Transformer language models. Despite its direct impact on prediction, this component has often been overlooked in analyzing Transformers. In this study, we investigate the inner workings of the prediction head, specifically focusing on bias parameters. Our e...", "track": "Student Research Workshop", "underline_id": 78207, "underline_url": "https://underline.io/events/395/posters/15227/poster/78207-your-spouse-needs-professional-help-determining-the-contextual-appropriateness-of-messages-through-modeling-social-relationships", "video_url": null }, "S12": { - "abstract": "We investigate the impact of different tokenizers on downstream performance in Japanese NLP, with the case of BERT architecture.", + "abstract": "This paper investigates the effect of tokenizers on the downstream performance of pretrained language models (PLMs) in scriptio continua languages where no explicit spaces exist between words, using Japanese as a case study. The tokenizer for such languages often consists of a morphological analyzer and a subword tokenizer, requiring us to conduct a comprehensive study of all possible pairs. However, previous studies lack this comprehensiveness. We therefore train extensive sets of tokenizers, build a PLM using each, and measure the downstream performance on a wide range of tasks. Our results demonstrate that each downstream task has a different optimal morphological analyzer, and that it is better to use Byte-Pair-Encoding or Unigram rather than WordPiece as a subword tokenizer, regardless of the type of task.", "anthology_url": "https://aclanthology.org/2023.acl-srw.5", "authors": [ "Takuro Fujii", @@ -79894,6 +86311,7 @@ "id": "S12", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.5.pdf", "paper_type": "srw", @@ -79903,14 +86321,14 @@ "similar_paper_ids": [], "slides_pdf": "https://assets.underline.io/lecture/78199/slideshow/bd5dce00c542a91ae260fc79278353f6.pdf", "title": "[SRW] How do different tokenizers perform on downstream tasks in scriptio continua languages?: A case study in Japanese", - "tldr": "We investigate the impact of different tokenizers on downstream performance in Japanese NLP, with the case of BERT architecture....", + "tldr": "This paper investigates the effect of tokenizers on the downstream performance of pretrained language models (PLMs) in scriptio continua languages where no explicit spaces exist between words, using Japanese as a case study. The tokenizer for such languages often consists of a morphological analyzer...", "track": "Student Research Workshop", "underline_id": 78199, "underline_url": "https://underline.io/events/395/posters/15298/poster/78199-srw-how-do-different-tokenizers-perform-on-downstream-tasks-in-scriptio-continua-languagesquestion-a-case-study-in-japanese", "video_url": null }, "S122": { - "abstract": "Weak supervision for entity extraction from noisy text", + "abstract": "We propose a weak supervision pipeline SWEET for extracting person names, which combines simple antirules with multiple large language models fine-tuned on public NER benchmark datasets as well as a domain-specific dataset labelled with ChatGPT. The proposed training process addresses the major challenge of lack of training data in this domain, by effectively aggregating multiple weak signals. The proposed method SWEET outperforms the previous supervised state-of-the-art method for this task by 10% F1 score and better generalizes to the benchmark datasets.", "anthology_url": "https://aclanthology.org/2023.acl-srw.42", "authors": [ "Javin Liu", @@ -79929,6 +86347,7 @@ "id": "S122", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.42.pdf", "paper_type": "srw", @@ -79938,14 +86357,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] SWEET: Weakly Supervised Person Name Extraction for Fighting Human Trafficking", - "tldr": "Weak supervision for entity extraction from noisy text...", + "tldr": "We propose a weak supervision pipeline SWEET for extracting person names, which combines simple antirules with multiple large language models fine-tuned on public NER benchmark datasets as well as a domain-specific dataset labelled with ChatGPT. The proposed training process addresses the major chal...", "track": "Student Research Workshop", "underline_id": 78178, "underline_url": "https://underline.io/events/395/posters/15298/poster/78178-sweet-weakly-supervised-person-name-extraction-for-fighting-human-trafficking", "video_url": null }, "S123": { - "abstract": "Speeding up the inference of pretrained models by designing better intermediate early exits and a comparison-based early exiting mechanism", + "abstract": "Recently, dynamic early exiting has attracted much attention since it can accelerate the inference speed of pre-trained models (PTMs). However, previous work on early exiting has neglected the intermediate exits' architectural designs. In this work, we propose a novel framework, \\underline{L}earned \\underline{E}xits and \\underline{CO}mparison-based early exiting (LECO) to improve PTMs' early exiting performances. First, to fully uncover the potentials of multi-exit BERT, we design a novel search space for intermediate exits and employ the idea of differentiable neural architecture search (DNAS) to design proper exit architectures for different intermediate layers automatically. Second, we propose a simple-yet-effective comparison-based early exiting mechanism (COBEE), which can help PTMs achieve better performance and speedup tradeoffs. Extensive experiments show that our LECO achieves the SOTA performances for multi-exit BERT training and dynamic early exiting.", "anthology_url": "https://aclanthology.org/2023.acl-srw.43", "authors": [ "Jingfan Zhang", @@ -79962,6 +86381,7 @@ "id": "S123", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.43.pdf", "paper_type": "srw", @@ -79971,14 +86391,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] LECO: Improving Early Exiting via Learned Exits and Comparison-based Exiting Mechanism", - "tldr": "Speeding up the inference of pretrained models by designing better intermediate early exits and a comparison-based early exiting mechanism...", + "tldr": "Recently, dynamic early exiting has attracted much attention since it can accelerate the inference speed of pre-trained models (PTMs). However, previous work on early exiting has neglected the intermediate exits' architectural designs. In this work, we propose a novel framework, \\underline{L}earned ...", "track": "Student Research Workshop", "underline_id": 78194, "underline_url": "https://underline.io/events/395/sessions/15239/lecture/78194-srw-leco-improving-early-exiting-via-learned-exits-and-comparison-based-exiting-mechanism", "video_url": null }, "S124": { - "abstract": "This paper is about performing authorship attribution of long 19th-century novels using the GAN-BERT model, comparing author counts, author combinations and sample text sizes.", + "abstract": "Authorship attribution aims to identify the author of an anonymous text. The task becomes even more worthwhile when it comes to literary works. For example, pen names were commonly used by female authors in the 19th century resulting in some literary works being incorrectly attributed or claimed. With this motivation, we collated a dataset of late 19th century novels in English. Due to the imbalance in the dataset and the unavailability of enough data per author, we employed the GANBERT model along with data sampling strategies to fine-tune a transformer-based model for authorship attribution. Differently from the earlier studies on the GAN-BERT model, we conducted transfer learning on comparatively smaller author subsets to train more focused author-specific models yielding performance over 0.88 accuracy and F1 scores. Furthermore, we observed that increasing the sample size has a negative impact on the model's performance. Our research mainly contributes to the ongoing authorship attribution research using GAN-BERT architecture, especially in attributing disputed novelists in the late 19th century.", "anthology_url": "https://aclanthology.org/2023.acl-srw.44", "authors": [ "Kanishka Silva", @@ -79997,6 +86417,7 @@ "id": "S124", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.44.pdf", "paper_type": "srw", @@ -80006,14 +86427,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Authorship Attribution of Late 19th Century Novels using GAN-BERT", - "tldr": "This paper is about performing authorship attribution of long 19th-century novels using the GAN-BERT model, comparing author counts, author combinations and sample text sizes....", + "tldr": "Authorship attribution aims to identify the author of an anonymous text. The task becomes even more worthwhile when it comes to literary works. For example, pen names were commonly used by female authors in the 19th century resulting in some literary works being incorrectly attributed or claimed. Wi...", "track": "Student Research Workshop", "underline_id": 78208, "underline_url": "https://underline.io/events/395/posters/15240/poster/78208-authorship-attribution-of-late-19th-century-novels-using-gan-bert", "video_url": null }, "S127": { - "abstract": "We trained bilingual LMs with a scenario similar to human L2 acquisition and analyzed their cross-lingual transfer from linguistic perspectives.", + "abstract": "With the success of neural language models (LMs), their language acquisition has gained much attention. This work sheds light on the second language (L2) acquisition of LMs, while previous work has explored their first language (L1) acquisition. Specifically, we trained bilingual LMs with a scenario similar to human L2 acquisition and analyzed their cross-lingual transfer from linguistic perspectives.\nOur experiments demonstrated that the L1 pretraining accelerated their linguistic generalization in L2, and language transfer configurations (e.g., the L1 choice, presence of parallel texts) substantially affected their generalizations. These also highlight that their L2 acquisition is not human-like in particular aspects.", "anthology_url": "https://aclanthology.org/2023.acl-srw.45", "authors": [ "Miyu Oba", @@ -80030,6 +86451,7 @@ "id": "S127", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.45.pdf", "paper_type": "srw", @@ -80039,14 +86461,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Second Language Acquisition of Neural Language Models", - "tldr": "We trained bilingual LMs with a scenario similar to human L2 acquisition and analyzed their cross-lingual transfer from linguistic perspectives....", + "tldr": "With the success of neural language models (LMs), their language acquisition has gained much attention. This work sheds light on the second language (L2) acquisition of LMs, while previous work has explored their first language (L1) acquisition. Specifically, we trained bilingual LMs with a scenario...", "track": "Student Research Workshop", "underline_id": 78209, "underline_url": "https://underline.io/events/395/posters/15264/poster/78209-second-language-acquisition-of-neural-language-models", "video_url": null }, "S129": { - "abstract": "We collect how-to guides for different target audiences and investigate qualitative and quantitative differences.", + "abstract": "Instructional texts for specific target groups should ideally take into account the prior knowledge and needs of the readers in order to guide them efficiently to their desired goals. However, targeting specific groups also carries the risk of reflecting disparate social norms and subtle stereotypes. In this paper, we investigate the extent to which how-to guides from one particular platform, wikiHow, differ in practice depending on the intended audience. We conduct two case studies in which we examine qualitative features of texts written for specific audiences. In a generalization study, we investigate which differences can also be systematically demonstrated using computational methods. The results of our studies show that guides from wikiHow, like other text genres, are subject to subtle biases. We aim to raise awareness of these inequalities as a first step to addressing them in future work.", "anthology_url": "https://aclanthology.org/2023.acl-srw.46", "authors": [ "Nicola Fanton", @@ -80062,6 +86484,7 @@ "id": "S129", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.46.pdf", "paper_type": "srw", @@ -80071,14 +86494,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] How-to Guides for Specific Audiences: A Corpus and Initial Findings", - "tldr": "We collect how-to guides for different target audiences and investigate qualitative and quantitative differences....", + "tldr": "Instructional texts for specific target groups should ideally take into account the prior knowledge and needs of the readers in order to guide them efficiently to their desired goals. However, targeting specific groups also carries the risk of reflecting disparate social norms and subtle stereotypes...", "track": "Student Research Workshop", "underline_id": 78179, "underline_url": "https://underline.io/events/395/sessions/15239/lecture/78179-srw-how-to-guides-for-specific-audiences-a-corpus-and-initial-findings", "video_url": null }, "S133": { - "abstract": "A new framework in which when given a non-sarcastic text as input, the text is converted into a sarcastic one with emoji where the emoji will specifically help to identify the sarcastic intent of the text.", + "abstract": "Sarcasm is a form of figurative language that serves as a humorous tool for mockery and ridicule. We present a novel architecture for sarcasm generation with emoji from a non-sarcastic input sentence in English. We divide the generation task into two sub tasks: one for generating textual sarcasm and another for collecting emojis associated with those sarcastic sentences. Two key elements of sarcasm are incorporated into the textual sarcasm generation task: valence reversal and semantic incongruity with context, where the context may involve shared commonsense or general knowledge between the speaker and their audience. The majority of existing sarcasm generation works have focused on this textual form. However, in the real world, when written texts fall short of effectively capturing the emotional cues of spoken and face-to-face communication, people often opt for emojis to accurately express their emotions. Due to the wide range of applications of emojis, incorporating appropriate emojis to generate textual sarcastic sentences helps advance sarcasm generation. We conclude our study by evaluating the generated sarcastic sentences using human judgement. All the codes and data used in this study has been made publicly available.", "anthology_url": "https://aclanthology.org/2023.acl-srw.47", "authors": [ "Faria Binte Kader", @@ -80097,6 +86520,7 @@ "id": "S133", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.47.pdf", "paper_type": "srw", @@ -80106,14 +86530,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] \"When Words Fail, Emojis Prevail\": A Novel Architecture for Generating Sarcastic Sentences With Emoji Using Valence Reversal and Semantic Incongruity", - "tldr": "A new framework in which when given a non-sarcastic text as input, the text is converted into a sarcastic one with emoji where the emoji will specifically help to identify the sarcastic intent of the text....", + "tldr": "Sarcasm is a form of figurative language that serves as a humorous tool for mockery and ridicule. We present a novel architecture for sarcasm generation with emoji from a non-sarcastic input sentence in English. We divide the generation task into two sub tasks: one for generating textual sarcasm and...", "track": "Student Research Workshop", "underline_id": 78316, "underline_url": "https://underline.io/events/395/posters/15200/poster/78316-srw-when-words-fail-emojis-prevail-a-novel-architecture-for-generating-sarcastic-sentences-with-emoji-using-valence-reversal-and-semantic-incongruity", "video_url": null }, "S139": { - "abstract": "We propose a thesis in which we explore how evaluation and interpretability techniques could lead to better natural language generation systems.", + "abstract": "With the fast-growing popularity of current large pre-trained language models (LLMs), it is necessary to dedicate efforts to making them more reliable. In this thesis proposal, we aim to improve the reliability of natural language generation systems (NLG) by researching the semantic accuracy of their outputs. We look at this problem from the outside (evaluation) and from the inside (interpretability). We propose a novel method for evaluating semantic accuracy and discuss the importance of working towards a unified and objective benchmark for NLG metrics. We also review interpretability approaches which could help us pinpoint the sources of inaccuracies within the models and explore potential mitigation strategies.", "anthology_url": "https://aclanthology.org/2023.acl-srw.48", "authors": [ "Patricia Schmidtova" @@ -80127,6 +86551,7 @@ "id": "S139", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.48.pdf", "paper_type": "srw", @@ -80136,14 +86561,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Semantic Accuracy in Natural Language Generation: A Thesis Proposal", - "tldr": "We propose a thesis in which we explore how evaluation and interpretability techniques could lead to better natural language generation systems....", + "tldr": "With the fast-growing popularity of current large pre-trained language models (LLMs), it is necessary to dedicate efforts to making them more reliable. In this thesis proposal, we aim to improve the reliability of natural language generation systems (NLG) by researching the semantic accuracy of thei...", "track": "Student Research Workshop", "underline_id": 78226, "underline_url": "https://underline.io/events/395/posters/15264/poster/78226-semantic-accuracy-in-natural-language-generation-a-thesis-proposal", "video_url": null }, "S14": { - "abstract": "", + "abstract": "The goal of portfolio management is to maximize investment returns while reducing investment risk. Reinforcement learning-based portfolio management continuously seeks characteristics in past data to maximize returns. Existing financial text information processing methods are not optimized for investment decision events, making it difficult to accurately reflect the actual impact of financial text information on investment. Additionally, differences in the quality of financial text information result in noisy data. In view of these limitations, this research proposal has two main goals. The first goal is to find effective ways to extract signals present in the financial news and the second goal is to eventually use the signals from the news articles to improve portfolio management. More specifically, in our proposal we focus on analysing sentiment signals from the financial news articles and we observe the effect of the sentiment scores on reinforcement learning based portfolio management approaches. We did an initial investigation to observe the effect of sentiment scores in portfolio management. In our experiments, we randomly selected eight stocks from Dow Jones Industrial Average index for experiments and verified that our model can significantly improve cumulative returns 117.3% while reduce max drawdown 15.2%. It greatly improves the performance of reinforcement learning-based investment management methods.", "anthology_url": "https://aclanthology.org/2023.acl-srw.6", "authors": [ "Zhilu Zhang" @@ -80157,6 +86582,7 @@ "id": "S14", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.6.pdf", "paper_type": "srw", @@ -80166,14 +86592,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Improving Portfolio Management with Signals from Financial News", - "tldr": "...", + "tldr": "The goal of portfolio management is to maximize investment returns while reducing investment risk. Reinforcement learning-based portfolio management continuously seeks characteristics in past data to maximize returns. Existing financial text information processing methods are not optimized for inves...", "track": "Student Research Workshop", "underline_id": 78312, "underline_url": "https://underline.io/events/395/posters/15200/poster/78312-improving-portfolio-management-with-signals-from-financial-news", "video_url": null }, "S144": { - "abstract": "In order to ameliorate the issue of spurious correlation, in this paper, we propose a framework for MWP solvers based on the generation of linguistic variants of the problem text and introduce a dataset containing paraphrased, adversarial, and inverse variants of the MWPs.", + "abstract": "The art of mathematical reasoning stands as a fundamental pillar of intellectual progress and is a central catalyst in cultivating human ingenuity. Researchers have recently published a plethora of works centered around the task of solving Math Word Problems (MWP) \u2014 a crucial stride towards general AI. These existing models are susceptible to dependency on shallow heuristics and spurious correlations to derive the solution expressions. In order to ameliorate this issue, in this paper, we propose a framework for MWP solvers based on the generation of linguistic variants of the problem text. The approach involves solving each of the variant problems and electing the predicted expression with the majority of the votes. We use DeBERTa (Decoding-enhanced BERT with disentangled attention) as the encoder to leverage its rich textual representations and enhanced mask decoder to construct the solution expressions. Furthermore, we introduce a challenging dataset, ParaMAWPS, consisting of paraphrased, adversarial, and inverse variants of selectively sampled MWPs from the benchmark Mawps dataset. We extensively experiment on this dataset along with other benchmark datasets using some baseline MWP solver models. We show that training on linguistic variants of problem statements and voting on candidate predictions improve the mathematical reasoning and robustness of the model. We make our code and data publicly available.", "anthology_url": "https://aclanthology.org/2023.acl-srw.49", "authors": [ "Syed Rifat Raiyan", @@ -80192,6 +86618,7 @@ "id": "S144", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.49.pdf", "paper_type": "srw", @@ -80201,14 +86628,14 @@ "similar_paper_ids": [], "slides_pdf": "https://assets.underline.io/lecture/78195/slideshow/40428b42bf6463cd9614ab5cd202c76a.pdf", "title": "[SRW] Math Word Problem Solving by Generating Linguistic Variants of Problem Statements", - "tldr": "In order to ameliorate the issue of spurious correlation, in this paper, we propose a framework for MWP solvers based on the generation of linguistic variants of the problem text and introduce a dataset containing paraphrased, adversarial, and inverse variants of the MWPs....", + "tldr": "The art of mathematical reasoning stands as a fundamental pillar of intellectual progress and is a central catalyst in cultivating human ingenuity. Researchers have recently published a plethora of works centered around the task of solving Math Word Problems (MWP) \u2014 a crucial stride towards general ...", "track": "Student Research Workshop", "underline_id": 78195, "underline_url": "https://underline.io/events/395/posters/15240/poster/78195-srw-math-word-problem-solving-by-generating-linguistic-variants-of-problem-statements", "video_url": null }, "S145": { - "abstract": "Framework towards better real-world misinformation detection through investigation of generalization, soft classification, and GPT-4.", + "abstract": "Misinformation poses a critical societal challenge, and current approaches have yet to produce an effective solution. We propose focusing on generalization, soft classification, and leveraging recent large language models to create more practical tools in contexts where perfect predictions remain unattainable. We begin by demonstrating that GPT-4 and other language models can outperform existing methods in the literature. Next, we explore their generalization, revealing that GPT-4 and RoBERTa-large exhibit critical differences in failure modes, which offer potential for significant performance improvements. Finally, we show that these models can be employed in soft classification frameworks to better quantify uncertainty. We discover that models with inferior hard classification results can achieve superior soft classification performance. Overall, our research lays the groundwork for future tools that can drive real-world progress on misinformation.", "anthology_url": "https://aclanthology.org/2023.acl-srw.50", "authors": [ "Kellin Pelrine", @@ -80226,6 +86653,7 @@ "id": "S145", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.50.pdf", "paper_type": "srw", @@ -80235,14 +86663,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4", - "tldr": "Framework towards better real-world misinformation detection through investigation of generalization, soft classification, and GPT-4....", + "tldr": "Misinformation poses a critical societal challenge, and current approaches have yet to produce an effective solution. We propose focusing on generalization, soft classification, and leveraging recent large language models to create more practical tools in contexts where perfect predictions remain un...", "track": "Student Research Workshop", "underline_id": 78227, "underline_url": "https://underline.io/events/395/posters/15264/poster/78227-towards-reliable-misinformation-mitigation-generalization-uncertainty-and-gpt-4", "video_url": null }, "S19": { - "abstract": "The paper is about video QA. It utilizes SRL partitioning to improve multi-step attention and reasoning of the models to attend to different frames for different parts of the question.", + "abstract": "Event-Level Video Question Answering (EVQA) requires complex reasoning across video events to obtain the visual information needed to provide optimal answers. However, despite significant progress in model performance, few studies have focused on using the explicit semantic connections between the question and visual information especially at the event level. There is need for using such semantic connections to facilitate complex reasoning across video frames. Therefore, we propose a semantic-aware dynamic retrospective-prospective reasoning approach for video-based question answering. Specifically, we explicitly use the Semantic Role Labeling (SRL) structure of the question in the dynamic reasoning process where we decide to move to the next frame based on which part of the SRL structure (agent, verb, patient, etc.) of the question is being focused on. We conduct experiments on a benchmark EVQA dataset - TrafficQA. Results show that our proposed approach achieves superior performance compared to previous state-of-the-art models. Our code is publicly available at https://github.com/lyuchenyang/Semantic-aware-VideoQA.", "anthology_url": "https://aclanthology.org/2023.acl-srw.7", "authors": [ "Chenyang Lyu", @@ -80259,6 +86687,7 @@ "id": "S19", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.7.pdf", "paper_type": "srw", @@ -80268,14 +86697,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Semantic-Aware Dynamic Retrospective-Prospective Reasoning for Event-Level Video Question Answering", - "tldr": "The paper is about video QA. It utilizes SRL partitioning to improve multi-step attention and reasoning of the models to attend to different frames for different parts of the question....", + "tldr": "Event-Level Video Question Answering (EVQA) requires complex reasoning across video events to obtain the visual information needed to provide optimal answers. However, despite significant progress in model performance, few studies have focused on using the explicit semantic connections between the q...", "track": "Student Research Workshop", "underline_id": 78313, "underline_url": "https://underline.io/events/395/posters/15279/poster/78313-semantic-aware-dynamic-retrospective-prospective-reasoning-for-event-level-video-question-answering", "video_url": null }, "S2": { - "abstract": "ChatGPT Analysis", + "abstract": "Large-scale language models, like ChatGPT, have garnered significant media attention and stunned the public with their remarkable capacity for generating coherent text from short natural language prompts. In this paper, we aim to conduct a systematic inspection of ChatGPT's performance in two controllable generation tasks, with respect to ChatGPT's ability to adapt its output to different target audiences (expert vs. layman) and writing styles (formal vs. informal). Additionally, we evaluate the faithfulness of the generated text, and compare the model's performance with human-authored texts. Our findings indicate that the stylistic variations produced by humans are considerably larger than those demonstrated by ChatGPT, and the generated texts diverge from human samples in several characteristics, such as the distribution of word types. Moreover, we observe that ChatGPT sometimes incorporates factual errors or hallucinations when adapting the text to suit a specific style.", "anthology_url": "https://aclanthology.org/2023.acl-srw.1", "authors": [ "Dongqi Pu", @@ -80290,6 +86719,7 @@ "id": "S2", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.1.pdf", "paper_type": "srw", @@ -80299,14 +86729,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer", - "tldr": "ChatGPT Analysis...", + "tldr": "Large-scale language models, like ChatGPT, have garnered significant media attention and stunned the public with their remarkable capacity for generating coherent text from short natural language prompts. In this paper, we aim to conduct a systematic inspection of ChatGPT's performance in two contro...", "track": "Student Research Workshop", "underline_id": 78309, "underline_url": "https://underline.io/events/395/posters/15200/poster/78309-chatgpt-vs-human-authored-text-insights-into-controllable-text-summarization-and-sentence-style-transfer", "video_url": null }, "S20": { - "abstract": "We construct Jamp, which is a Japanese NLI dataset for temporal inference, and evaluate the generalization capacity of several LMs on our dataset.", + "abstract": "Natural Language Inference (NLI) tasks involving temporal inference remain challenging for pre-trained language models (LMs). Although various datasets have been created for this task, they primarily focus on English and do not address the need for resources in other languages. It is unclear whether current LMs realize the generalization capacity for temporal inference across languages. In this paper, we present Jamp, a Japanese NLI benchmark focused on temporal inference. Our dataset includes a range of temporal inference patterns, which enables us to conduct fine-grained analysis. To begin the data annotation process, we create diverse inference templates based on the formal semantics test suites. We then automatically generate diverse NLI examples by using the Japanese case frame dictionary and well-designed templates while controlling the distribution of inference patterns and gold labels. We evaluate the generalization capacities of monolingual/multilingual LMs by splitting our dataset based on tense fragments (i.e., temporal inference patterns). Our findings demonstrate that LMs struggle with specific linguistic phenomena, such as habituality, indicating that there is potential for the development of more effective NLI models across languages.", "anthology_url": "https://aclanthology.org/2023.acl-srw.8", "authors": [ "Tomoki Sugimoto", @@ -80322,6 +86752,7 @@ "id": "S20", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.8.pdf", "paper_type": "srw", @@ -80331,14 +86762,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models", - "tldr": "We construct Jamp, which is a Japanese NLI dataset for temporal inference, and evaluate the generalization capacity of several LMs on our dataset....", + "tldr": "Natural Language Inference (NLI) tasks involving temporal inference remain challenging for pre-trained language models (LMs). Although various datasets have been created for this task, they primarily focus on English and do not address the need for resources in other languages. It is unclear whether...", "track": "Student Research Workshop", "underline_id": 78200, "underline_url": "https://underline.io/events/395/posters/15227/poster/78200-jamp-controlled-japanese-temporal-inference-dataset-for-evaluating-generalization-capacity-of-language-models", "video_url": null }, "S21": { - "abstract": "Reading and Translating Classical Chinese in Japanese Methods by Language Models", + "abstract": "Recent studies in natural language processing (NLP) have focused on modern languages and achieved state-of-the-art results in many tasks.\nMeanwhile, little attention has been paid to ancient texts and related tasks. \nClassical Chinese first came to Japan approximately 2,000 years ago.\nIt was gradually adapted to a Japanese form called Kanbun-Kundoku (Kanbun) in Japanese reading and translating methods, which has significantly impacted Japanese literature.\nHowever, compared to the rich resources of ancient texts in mainland China, Kanbun resources remain scarce in Japan.\nTo solve this problem, we construct the first Classical-Chinese-to-Kanbun dataset in the world.\nFurthermore, we introduce two tasks, character reordering and machine translation, both of which play a significant role in Kanbun comprehension.\nWe also test the current language models on these tasks and discuss the best evaluation method by comparing the results with human scores.\nWe release our code and dataset on GitHub.", "anthology_url": "https://aclanthology.org/2023.acl-srw.9", "authors": [ "Hao Wang", @@ -80354,6 +86785,7 @@ "id": "S21", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.9.pdf", "paper_type": "srw", @@ -80363,14 +86795,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Kanbun-LM: Reading and Translating Classical Chinese in Japanese Methods by Language Models", - "tldr": "Reading and Translating Classical Chinese in Japanese Methods by Language Models...", + "tldr": "Recent studies in natural language processing (NLP) have focused on modern languages and achieved state-of-the-art results in many tasks.\nMeanwhile, little attention has been paid to ancient texts and related tasks. \nClassical Chinese first came to Japan approximately 2,000 years ago.\nIt was gradual...", "track": "Student Research Workshop", "underline_id": 78201, "underline_url": "https://underline.io/events/395/posters/15298/poster/78201-kanbun-lm-reading-and-translating-classical-chinese-in-japanese-methods-by-language-models", "video_url": null }, "S24": { - "abstract": "We create a multilingual code search dataset by translating the existing English dataset with a machine translation model and conduct a baseline experiment on a code search task.", + "abstract": "Code search is a task to find programming codes that semantically match the given natural language queries. \nEven though some of the existing datasets for this task are multilingual on the programming language side, their query data are only in English. \nIn this research, we create a multilingual code search dataset in four natural and four programming languages using a neural machine translation model.\nUsing our dataset, we pre-train and fine-tune the Transformer-based models and then evaluate them on multiple code search test sets.\nOur results show that the model pre-trained with all natural and programming language data has performed best in most cases. \nBy applying back-translation data filtering to our dataset, we demonstrate that the translation quality affects the model's performance to a certain extent, but the data size matters more.", "anthology_url": "https://aclanthology.org/2023.acl-srw.10", "authors": [ "Ryo Sekizawa", @@ -80387,6 +86819,7 @@ "id": "S24", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.10.pdf", "paper_type": "srw", @@ -80396,14 +86829,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Constructing Multilingual Code Search Dataset Using Neural Machine Translation", - "tldr": "We create a multilingual code search dataset by translating the existing English dataset with a machine translation model and conduct a baseline experiment on a code search task....", + "tldr": "Code search is a task to find programming codes that semantically match the given natural language queries. \nEven though some of the existing datasets for this task are multilingual on the programming language side, their query data are only in English. \nIn this research, we create a multilingual co...", "track": "Student Research Workshop", "underline_id": 78213, "underline_url": "https://underline.io/events/395/posters/15227/poster/78213-constructing-multilingual-code-search-dataset-using-neural-machine-translation", "video_url": null }, "S25": { - "abstract": "While NLP models of bilingual code-switching have utilized word-level tokens, this work advocates for the Intonation Unit, a multi-word prosodic unit, by demonstrating how metrics of code-switching complexity are impacted by the distinction between other-language single-word items and multi-word strings.", + "abstract": "Models and metrics of linguistic code-switching (CS) have almost exclusively worked with word-level units. However, any two words are not equally likely CS points in bilingual speech. In addition, other-language single-word items and alternating-language multi-word items have distinct properties. Adapting these familiar metrics to the Intonation Unit (IU), we capture a shared tendency for CS to occur across rather than within prosodic boundaries. This constraint is distorted when single- and multi-word other-language items are merged. Individual differences according to language distribution and CS rates are independent, visualized in the number and breadth of language bands in transcripts of bilingual speech. These results are important to consider in future development of code-switched datasets for NLP tasks, as the IU token and exclusion/inclusion of single-word items highly impacts the CS represented in the input text.", "anthology_url": "https://aclanthology.org/2023.acl-srw.11", "authors": [ "Rebecca Pattichis", @@ -80420,6 +86853,7 @@ "id": "S25", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.11.pdf", "paper_type": "srw", @@ -80429,14 +86863,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Aligning Code-Switching Metrics with Bilingual Behavior", - "tldr": "While NLP models of bilingual code-switching have utilized word-level tokens, this work advocates for the Intonation Unit, a multi-word prosodic unit, by demonstrating how metrics of code-switching complexity are impacted by the distinction between other-language single-word items and multi-word str...", + "tldr": "Models and metrics of linguistic code-switching (CS) have almost exclusively worked with word-level units. However, any two words are not equally likely CS points in bilingual speech. In addition, other-language single-word items and alternating-language multi-word items have distinct properties. Ad...", "track": "Student Research Workshop", "underline_id": 78171, "underline_url": "https://underline.io/events/395/posters/15298/poster/78171-aligning-code-switching-metrics-with-bilingual-behavior", "video_url": null }, "S28": { - "abstract": "This study proposes a new multimodal neural machine translation model using synthetic images transformed by a latent diffusion model.", + "abstract": "This study proposes a new multimodal neural machine translation (MNMT) model using synthetic images transformed by a latent diffusion model. MNMT translates a source language sentence based on its related image, but the image usually contains noisy information that are not relevant to the source language sentence.Our proposed method first generates a synthetic image corresponding to the content of the source language sentence by using a latent diffusion model and then performs translation based on the synthetic image. The experiments on the English-German translation tasks using the Multi30k dataset demonstrate the effectiveness of the proposed method.", "anthology_url": "https://aclanthology.org/2023.acl-srw.12", "authors": [ "Ryoya Yuasa", @@ -80454,6 +86888,7 @@ "id": "S28", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.12.pdf", "paper_type": "srw", @@ -80463,14 +86898,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Multimodal Neural Machine Translation Using Synthetic Images Transformed by Latent Diffusion Model", - "tldr": "This study proposes a new multimodal neural machine translation model using synthetic images transformed by a latent diffusion model....", + "tldr": "This study proposes a new multimodal neural machine translation (MNMT) model using synthetic images transformed by a latent diffusion model. MNMT translates a source language sentence based on its related image, but the image usually contains noisy information that are not relevant to the source lan...", "track": "Student Research Workshop", "underline_id": 78172, "underline_url": "https://underline.io/events/395/posters/15227/poster/78172-multimodal-neural-machine-translation-using-synthetic-images-transformed-by-latent-diffusion-model", "video_url": null }, "S29": { - "abstract": "In this work, we use word alignments computed over large scale bilingual corpora to develop predictors of lexical translation difficulty.", + "abstract": "Translation difficulty arises when translators are required to resolve translation ambiguity from multiple possible translations. Translation difficulty can be measured by recording the diversity of responses provided by human translators and the time taken to provide these responses, but these behavioral measures are costly and do not scale. In this work, we use word alignments computed over large scale bilingual corpora to develop predictors of lexical translation difficulty. We evaluate our approach using behavioural data from translations provided both in and out of context, and report results that improve on a previous embedding based approach (Thompson et al., 2020). Our work can therefore contribute to a deeper understanding of cross-lingual differences and of causes of translation difficulty.", "anthology_url": "https://aclanthology.org/2023.acl-srw.13", "authors": [ "Zheng Wei Lim", @@ -80487,6 +86922,7 @@ "id": "S29", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.13.pdf", "paper_type": "srw", @@ -80496,14 +86932,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Predicting Human Translation Difficulty Using Automatic Word Alignment", - "tldr": "In this work, we use word alignments computed over large scale bilingual corpora to develop predictors of lexical translation difficulty....", + "tldr": "Translation difficulty arises when translators are required to resolve translation ambiguity from multiple possible translations. Translation difficulty can be measured by recording the diversity of responses provided by human translators and the time taken to provide these responses, but these beha...", "track": "Student Research Workshop", "underline_id": 78202, "underline_url": "https://underline.io/events/395/posters/15227/poster/78202-predicting-human-translation-difficulty-using-automatic-word-alignment", "video_url": null }, "S3": { - "abstract": "Representation learning of Sinitic Phonology using a knowledge graph based method", + "abstract": "Machine learning techniques have shown their competence for representing and reasoning in symbolic systems such as language and phonology. In Sinitic Historical Phonology, notable tasks that could benefit from machine learning include the comparison of dialects and reconstruction of proto-languages systems. Motivated by this, this paper provides an approach for obtaining multi-dialectal representations of Sinitic syllables, by constructing a knowledge graph from structured phonological data ,then applying the BoxE technique from knowledge base learning. We applied unsupervised clustering techniques to the obtained representations to observe that the representations capture phonemic contrast from the input dialects. Furthermore, we trained classifiers to perform inference of unobserved Middle Chinese labels, showing the representations' potential for indicating archaic, proto-language features. The representations can be used for performing completion of fragmented Sinitic phonological knowledge bases, estimating divergences between different characters, or aiding the exploration and reconstruction of archaic features.", "anthology_url": "https://aclanthology.org/2023.acl-srw.2", "authors": [ "Zhibai Jia" @@ -80517,6 +86953,7 @@ "id": "S3", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.2.pdf", "paper_type": "srw", @@ -80526,14 +86963,14 @@ "similar_paper_ids": [], "slides_pdf": "https://assets.underline.io/lecture/78198/slideshow/92e6804ac76cbd9ee35c372f5c26d463.pdf", "title": "[SRW] Multi-Dialectal Representation Learning of Sinitic Phonology", - "tldr": "Representation learning of Sinitic Phonology using a knowledge graph based method...", + "tldr": "Machine learning techniques have shown their competence for representing and reasoning in symbolic systems such as language and phonology. In Sinitic Historical Phonology, notable tasks that could benefit from machine learning include the comparison of dialects and reconstruction of proto-languages ...", "track": "Student Research Workshop", "underline_id": 78198, "underline_url": "https://underline.io/events/395/posters/15254/poster/78198-srw-multi-dialectal-representation-learning-of-sinitic-phonology", "video_url": null }, "S32": { - "abstract": "Using the top 100 YouTube Kids Channel", + "abstract": "The internet provides a platform to build a world for children that is free of harmful social biases. However, it also presents an opportunity to generate and distribute unregulated content with minimal guidance from platforms, leading to the perpetuation or amplification of the very same biases we aim to mitigate. This study investigates the prevalence of social biases in children's videos on the top 100 YouTube Kids (YTK) channels having over 80,000 videos. Using video transcripts and picture frames, we examine gender and occupational stereotypes. Results from the Word Embedding Association Test and similar bias assessments reveal clear gender biases in many occupations, STEM-related fields, etc. We also find that video frames display more significant gender biases than video transcripts, suggesting that women are underrepresented in occupational discussions. The study's findings are important for content creators, video platform providers, and policymakers, highlighting a need for more vigilance in creating and recommending kids' content. We further propose a multi-modal bias metric that combines language and visual biases.", "anthology_url": "https://aclanthology.org/2023.acl-srw.14", "authors": [ "Tiasa Singha Roy", @@ -80550,6 +86987,7 @@ "id": "S32", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.14.pdf", "paper_type": "srw", @@ -80559,14 +86997,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Gender Stereotyping in Popular Children's Videos", - "tldr": "Using the top 100 YouTube Kids Channel...", + "tldr": "The internet provides a platform to build a world for children that is free of harmful social biases. However, it also presents an opportunity to generate and distribute unregulated content with minimal guidance from platforms, leading to the perpetuation or amplification of the very same biases we ...", "track": "Student Research Workshop", "underline_id": 78214, "underline_url": "https://underline.io/events/395/posters/15264/poster/78214-srw-gender-stereotyping-in-popular-children-s-videos", "video_url": null }, "S34": { - "abstract": "This paper introduces a confidence-based syntax encoding network (cSEN) to incorporate syntax in ancient Chinese understanding tasks, effectively improving performance by mitigating noise and incompatibility issues.", + "abstract": "Despite the rapid development of neural-based models, syntax still plays a crucial role in modern natural language processing. However, few studies have incorporated syntactic information into ancient Chinese understanding tasks due to the lack of syntactic annotation. This paper explores the role of syntax in ancient Chinese understanding based on the noisy syntax trees from unsupervised derivation and modern Chinese syntax parsers. On top of that, we propose a novel syntax encoding component -- confidence-based syntax encoding network (cSEN) to alleviate the side effects from the existing noise caused by unsupervised syntax derivation and the incompatibility between ancient and modern Chinese. Experiments on two typical ancient Chinese understanding tasks, ancient poetry theme classification and ancient-modern Chinese translation, demonstrate that syntactic information can effectively enhance the understanding of ancient Chinese over strong baselines, and that the proposed cSEN plays an important role in noisy scenarios.", "anthology_url": "https://aclanthology.org/2023.acl-srw.15", "authors": [ "Ping Wang", @@ -80583,6 +87021,7 @@ "id": "S34", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.15.pdf", "paper_type": "srw", @@ -80592,14 +87031,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Enhancing Ancient Chinese Understanding with Derived Noisy Syntax Trees", - "tldr": "This paper introduces a confidence-based syntax encoding network (cSEN) to incorporate syntax in ancient Chinese understanding tasks, effectively improving performance by mitigating noise and incompatibility issues....", + "tldr": "Despite the rapid development of neural-based models, syntax still plays a crucial role in modern natural language processing. However, few studies have incorporated syntactic information into ancient Chinese understanding tasks due to the lack of syntactic annotation. This paper explores the role o...", "track": "Student Research Workshop", "underline_id": 78215, "underline_url": "https://underline.io/events/395/posters/15298/poster/78215-enhancing-ancient-chinese-understanding-with-derived-noisy-syntax-trees", "video_url": null }, "S36": { - "abstract": "This paper suggests that anisotropy may be an inherent property of Transformers-based models, and extends to other modalities beyond token-based NLP.", + "abstract": "The representation degeneration problem is a phenomenon that is widely observed among self-supervised learning methods based on Transformers. In NLP, it takes the form of anisotropy, a singular property of hidden representations which makes them unexpectedly close to each other in terms of angular distance (cosine-similarity). Some recent works tend to show that anisotropy is a consequence of optimizing the cross-entropy loss on long-tailed distributions of tokens. We show in this paper that anisotropy can also be observed empirically in language models with specific objectives that should not suffer directly from the same consequences. We also show that the anisotropy problem extends to Transformers trained on other modalities. Our observations tend to demonstrate that anisotropy might actually be inherent to Transformers-based models.", "anthology_url": "https://aclanthology.org/2023.acl-srw.16", "authors": [ "Nathan Godey", @@ -80615,6 +87054,7 @@ "id": "S36", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.16.pdf", "paper_type": "srw", @@ -80624,14 +87064,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Is Anisotropy Inherent to Transformers?", - "tldr": "This paper suggests that anisotropy may be an inherent property of Transformers-based models, and extends to other modalities beyond token-based NLP....", + "tldr": "The representation degeneration problem is a phenomenon that is widely observed among self-supervised learning methods based on Transformers. In NLP, it takes the form of anisotropy, a singular property of hidden representations which makes them unexpectedly close to each other in terms of angular d...", "track": "Student Research Workshop", "underline_id": 78203, "underline_url": "https://underline.io/events/395/posters/15227/poster/78203-is-anisotropy-inherent-to-transformersquestion", "video_url": null }, "S38": { - "abstract": "We explored the generation of NPC dialogue using a zero-shot prompting method as well as the ability of LMs to self-evaluate and score dialogue with few-shot learning.", + "abstract": "In this paper, we study the viability of the deployment of language models towards non-playable character (NPC) scripts, by introducing a novel pipeline for the automatic construction of NPC scripts using Transformer-based believable scripts for a variety of game genres and specifications. In addition, we propose a self-diagnosis method inspired by previous work to develop language models, tailored specifically to desirable NPC qualities such as coherency, believability, and degree of repetition. Finally, we propose a new benchmark, called The Turing Quest, which we use to show that the pipeline, when applied to GPT-3, can generate for a variety of game genres and contexts, NPC scripts that can fool judges in thinking they have been written by humans. We believe that these findings can greatly benefit both the gaming industry and its global community of users, since many current games continue to base their NPCs on manually-curated scripts that are resource-demanding and may curb the immersiveness and enjoyment of the user.", "anthology_url": "https://aclanthology.org/2023.acl-srw.17", "authors": [ "Qi Chen Gao", @@ -80646,6 +87086,7 @@ "id": "S38", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.17.pdf", "paper_type": "srw", @@ -80655,14 +87096,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] The Turing Quest: Can Transformers Make Good NPCs?", - "tldr": "We explored the generation of NPC dialogue using a zero-shot prompting method as well as the ability of LMs to self-evaluate and score dialogue with few-shot learning....", + "tldr": "In this paper, we study the viability of the deployment of language models towards non-playable character (NPC) scripts, by introducing a novel pipeline for the automatic construction of NPC scripts using Transformer-based believable scripts for a variety of game genres and specifications. In additi...", "track": "Student Research Workshop", "underline_id": 78216, "underline_url": "https://underline.io/events/395/posters/15264/poster/78216-the-turing-quest-can-transformers-make-good-npcsquestion", "video_url": null }, "S40": { - "abstract": "We propose a data-driven framework to select clinical note sections with high predictive power.", + "abstract": "Recent advances in large language models have led to renewed interest in natural language processing in healthcare using the free text of clinical notes. One distinguishing characteristic of clinical notes is their long time span over multiple long documents. The unique structure of clinical notes creates a new design choice: when the context length for a language model predictor is limited, which part of clinical notes should we choose as the input? Existing studies either choose the inputs with domain knowledge or simply truncate them. We propose a framework to analyze the sections with high predictive power. Using MIMIC-III, we show that: 1) predictive power distribution is different between nursing notes and discharge notes and 2) combining different types of notes could improve performance when the context length is large. Our findings suggest that a carefully selected sampling function could enable more efficient information extraction from clinical notes.", "anthology_url": "https://aclanthology.org/2023.acl-srw.18", "authors": [ "Hongyi Zheng", @@ -80680,6 +87121,7 @@ "id": "S40", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.18.pdf", "paper_type": "srw", @@ -80689,14 +87131,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Making the Most Out of the Limited Context Length: Predictive Power Varies with Clinical Note Type and Note Section", - "tldr": "We propose a data-driven framework to select clinical note sections with high predictive power....", + "tldr": "Recent advances in large language models have led to renewed interest in natural language processing in healthcare using the free text of clinical notes. One distinguishing characteristic of clinical notes is their long time span over multiple long documents. The unique structure of clinical notes c...", "track": "Student Research Workshop", "underline_id": 78217, "underline_url": "https://underline.io/events/395/posters/15264/poster/78217-making-the-most-out-of-the-limited-context-length-predictive-power-varies-with-clinical-note-type-and-note-section", "video_url": null }, "S41": { - "abstract": "This paper investigates the usefulness of correlation bias in improving language models' performance on predicting imbalanced clinical codes from discharge summaries.", + "abstract": "The Ninth Revision of the International Classification of Diseases (ICD-9) is a standardized coding system used to classify health conditions. It is used for billing, tracking individual patient conditions, and for epidemiology. The highly detailed and technical nature of the codes and their associated medical conditions make it difficult for humans to accurately record them. Researchers have explored the use of neural networks, particularly language models, for automated ICD-9 code assignment. However, the imbalanced distribution of ICD-9 codes leads to poor performance. One solution is to use domain knowledge to incorporate a useful prior. This paper evaluates the usefulness of the correlation bias: we hypothesize that correlations between ICD-9 codes and other medical codes could help improve language models' performance. We showed that while the correlation bias worsens the overall performance, the effect on individual class can be negative or positive. Performance on classes that are more imbalanced and less correlated with other codes is more sensitive to incorporating the correlation bias. This suggests that while the correlation bias has potential to improve ICD-9 code assignment in certain cases, the applicability criteria need to be more carefully studied.", "anthology_url": "https://aclanthology.org/2023.acl-srw.19", "authors": [ "Zihao Yang", @@ -80716,6 +87158,7 @@ "id": "S41", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.19.pdf", "paper_type": "srw", @@ -80725,14 +87168,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Intriguing Effect of the Correlation Prior on ICD-9 Code Assignment", - "tldr": "This paper investigates the usefulness of correlation bias in improving language models' performance on predicting imbalanced clinical codes from discharge summaries....", + "tldr": "The Ninth Revision of the International Classification of Diseases (ICD-9) is a standardized coding system used to classify health conditions. It is used for billing, tracking individual patient conditions, and for epidemiology. The highly detailed and technical nature of the codes and their associa...", "track": "Student Research Workshop", "underline_id": 78218, "underline_url": "https://underline.io/events/395/posters/15264/poster/78218-intriguing-effect-of-the-correlation-prior-on-icd-9-code-assignment", "video_url": null }, "S43": { - "abstract": "The paper evaluates existing OOD detection methods for NLP systems and emphasizes the need for further research to develop more effective approaches to ensure safety and trustworthiness of NLP systems.", + "abstract": "State-of-the-art models can perform well in controlled environments, but they often struggle when presented with out-of-distribution (OOD) examples, making OOD detection a critical component of NLP systems. In this paper, we focus on highlighting the limitations of existing approaches to OOD detection in NLP. Specifically, we evaluated eight OOD detection methods that are easily integrable into existing NLP systems and require no additional OOD data or model modifications. One of our contributions is providing a well-structured research environment that allows for full reproducibility of the results. Additionally, our analysis shows that existing OOD detection methods for NLP tasks are not yet sufficiently sensitive to capture all samples characterized by various types of distributional shifts. Particularly challenging testing scenarios arise in cases of background shift and randomly shuffled word order within in domain texts. This highlights the need for future work to develop more effective OOD detection approaches for the NLP problems, and our work provides a well-defined foundation for further research in this area.", "anthology_url": "https://aclanthology.org/2023.acl-srw.20", "authors": [ "Mateusz Baran", @@ -80750,6 +87193,7 @@ "id": "S43", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.20.pdf", "paper_type": "srw", @@ -80759,14 +87203,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Classical Out-of-Distribution Detection Methods Benchmark in Text Classification Tasks", - "tldr": "The paper evaluates existing OOD detection methods for NLP systems and emphasizes the need for further research to develop more effective approaches to ensure safety and trustworthiness of NLP systems....", + "tldr": "State-of-the-art models can perform well in controlled environments, but they often struggle when presented with out-of-distribution (OOD) examples, making OOD detection a critical component of NLP systems. In this paper, we focus on highlighting the limitations of existing approaches to OOD detecti...", "track": "Student Research Workshop", "underline_id": 78190, "underline_url": "https://underline.io/events/395/posters/15254/poster/78190-classical-out-of-distribution-detection-methods-benchmark-in-text-classification-tasks", "video_url": null }, "S46": { - "abstract": "We propose a state-vector framework to analyze the effects of datasets on deep learning language models using probing tasks.", + "abstract": "The impressive recent performance of DNN-based systems can be partially attributed to high-quality datasets -- indeed -- often, multiple at once. However, the effects of the datasets, especially how they interact with each other, are not studied well. We propose a state-vector framework to quantify the effect of the data itself. This framework uses idealized probing task results as the bases of the vector space and allows us to quantify the individual and interaction effects of datasets. We show that the significant effects of some commonly-used language understanding datasets are characteristic, and are concentrated on a few linguistic dimensions. Additionally, we observe some ``spill-over'' effects: the datasets could impact the models along dimensions that might appear irrelevant to the intended tasks. Our state-vector framework provides a systematic approach to the study of the effects of data, a crucial component in responsible model development.", "anthology_url": "https://aclanthology.org/2023.acl-srw.21", "authors": [ "Esmat Sahak", @@ -80782,6 +87226,7 @@ "id": "S46", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.21.pdf", "paper_type": "srw", @@ -80791,14 +87236,14 @@ "similar_paper_ids": [], "slides_pdf": "https://assets.underline.io/lecture/78204/slideshow/27f71558921f1b175cdff68e8e3f1411.pdf", "title": "[SRW] A State-Vector Framework For Dataset Effects", - "tldr": "We propose a state-vector framework to analyze the effects of datasets on deep learning language models using probing tasks....", + "tldr": "The impressive recent performance of DNN-based systems can be partially attributed to high-quality datasets -- indeed -- often, multiple at once. However, the effects of the datasets, especially how they interact with each other, are not studied well. We propose a state-vector framework to quantify ...", "track": "Student Research Workshop", "underline_id": 78204, "underline_url": "https://underline.io/events/395/posters/15254/poster/78204-srw-a-state-vector-framework-for-dataset-effects", "video_url": null }, "S47": { - "abstract": "Our study aimed to explore the feasibility of using LMs as KBs, and we focused specifically on 1-to-N relational knowledge, an area that has not been extensively researched, and proposed a comprehensive approach that involved identifying the unique characteristics of this type of knowledge, designing appropriate training methods, and developing evaluation perspectives.", + "abstract": "It has been suggested that pretrained language models can be viewed as knowledge bases.\nOne of the prerequisites for using language models as knowledge bases is how accurately they can store and retrieve world knowledge. It is already revealed that language models can store much 1-to-1 relational knowledge, such as ''country and its capital,'' with high memorization accuracy.\n\nOn the other hand, world knowledge includes not only 1-to-1 but also 1-to-N relational knowledge, such as ''parent and children.''\nHowever, it is not clear how accurately language models can handle 1-to-N relational knowledge.\nTo investigate language models' abilities toward 1-to-N relational knowledge, we start by designing the problem settings. Specifically, we organize the character of 1-to-N relational knowledge and define two essential skills: (i) memorizing multiple objects individually and (ii) retrieving multiple stored objects without excesses or deficiencies at once. We inspect LMs' ability to handle 1-to-N relational knowledge on the controlled synthesized data.\n\nAs a result, we report that it is possible to memorize multiple objects with high accuracy, but generalizing the retrieval ability (expressly, enumeration) is challenging.", "anthology_url": "https://aclanthology.org/2023.acl-srw.22", "authors": [ "Haruki Nagasawa", @@ -80815,6 +87260,7 @@ "id": "S47", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.22.pdf", "paper_type": "srw", @@ -80824,14 +87270,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Can LMs Store and Retrieve 1-to-N Relational Knowledge?", - "tldr": "Our study aimed to explore the feasibility of using LMs as KBs, and we focused specifically on 1-to-N relational knowledge, an area that has not been extensively researched, and proposed a comprehensive approach that involved identifying the unique characteristics of this type of knowledge, designin...", + "tldr": "It has been suggested that pretrained language models can be viewed as knowledge bases.\nOne of the prerequisites for using language models as knowledge bases is how accurately they can store and retrieve world knowledge. It is already revealed that language models can store much 1-to-1 relational kn...", "track": "Student Research Workshop", "underline_id": 78191, "underline_url": "https://underline.io/events/395/posters/15264/poster/78191-can-lms-store-and-retrieve-1-to-n-relational-knowledgequestion", "video_url": null }, "S48": { - "abstract": "The paper assesses whether masked language models can distinguish entities in their internal representations, finding they can to a certain degree, even when there was variation in the surrounding context and mentions.", + "abstract": "This paper addresses whether masked language models can distinguish (named) entities in internal representations and investigates whether the model's internal representations of the same entity create a sufficiently compact cluster that is distinct from others.\nThe primary contributions of the paper are two-fold. \nFirst, we present a novel method for quantitatively assessing the degree to which pre-trained masked language models distinguish between entities in the internal representations. \nOur approach investigates whether entities belonging to the same object are separated from other embeddings in the embedding space.\nSecond, we conducted experiments, and the findings revealed that internal representations of masked language models were able to distinguish entities from other concepts to a certain degree, even when there was variation in the surrounding context and mentions. (e.g., BERT could distinguish about 70% of the entities from other concepts.)\nAn implication of these results is the possibility that the distinct entities, as reflected in internal representations, are considered evidence of behavior that effectively processes entity strings.", "anthology_url": "https://aclanthology.org/2023.acl-srw.23", "authors": [ "Masaki Sakata", @@ -80848,6 +87294,7 @@ "id": "S48", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.23.pdf", "paper_type": "srw", @@ -80857,14 +87304,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Geometric Locality of Entity Embeddings in Masked Language Models", - "tldr": "The paper assesses whether masked language models can distinguish entities in their internal representations, finding they can to a certain degree, even when there was variation in the surrounding context and mentions....", + "tldr": "This paper addresses whether masked language models can distinguish (named) entities in internal representations and investigates whether the model's internal representations of the same entity create a sufficiently compact cluster that is distinct from others.\nThe primary contributions of the paper...", "track": "Student Research Workshop", "underline_id": 78219, "underline_url": "https://underline.io/events/395/posters/15227/poster/78219-geometric-locality-of-entity-embeddings-in-masked-language-models", "video_url": null }, "S5": { - "abstract": "Automated Identification of Multilingual Abusive Content on Social Media", + "abstract": "Abusive language and hate speech in tweets and comments on various social media platforms have become ubiquitous. This paper focuses on classifying an input text in English, Hindi and Hinglish (Hindi written in English) along with emojis and emoticons into three categories - Abusive, Hate Speech or Neither based on the confidence scores of each class. Abusive language on social media is textual content that contains disparaging words. Hate speech can be threatening and can hurt the sentiments of a person or a community. To gain better insights, results are captured with and without lemmatization and stemming. Accuracy is used as the metric to evaluate the classification models. Deep learning models BERT and RoBERTa are trained separately on plain English data (PE), Hinglish data along with emojis (HEmo) and the combined full dataset (CF). The same process is repeated for five non-deep learning models - Decision trees, k-Nearest Neighbours (kNN), Support Vector Machines (SVM), Random Forest and Na\u00efve Bayes. The deep learning model RoBERTa results in the highest accuracy of 0.9938 on the PE dataset, 0.9896 on the EHEmo dataset and 0.9899 on the CF dataset. SVM model performed best among traditional classifiers with an accuracy of 0.7996 on the PE dataset, 0.7899 on the EHEmo dataset and 0.7913 on the CF dataset.", "anthology_url": "https://aclanthology.org/2023.acl-srw.3", "authors": [ "Sneha P", @@ -80882,6 +87329,7 @@ "id": "S5", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.3.pdf", "paper_type": "srw", @@ -80891,14 +87339,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Detection and Comparison of Abusive and Hate Speech in English and Hinglish with Emojis Using Deep Learning and Non-Deep Learning Techniques", - "tldr": "Automated Identification of Multilingual Abusive Content on Social Media...", + "tldr": "Abusive language and hate speech in tweets and comments on various social media platforms have become ubiquitous. This paper focuses on classifying an input text in English, Hindi and Hinglish (Hindi written in English) along with emojis and emoticons into three categories - Abusive, Hate Speech or ...", "track": "Student Research Workshop", "underline_id": 78310, "underline_url": "https://underline.io/events/395/posters/15240/poster/78310-detection-and-comparison-of-abusive-and-hate-speech-in-english-and-hinglish-with-emojis-using-deep-learning-and-non-deep-learning-techniques", "video_url": null }, "S50": { - "abstract": "This study investigates whether and how theoretical linguistics improves language clustering for multilingual named entity recognition (NER), with the two types of language groupings proposed: one based on morpho-syntactic features in a nominal domain and one based on a head parameter.", + "abstract": "While embedding-based methods have been dominant in language clustering for multilingual tasks, clustering based on linguistic features has not yet been explored much, as it remains baselines (Tan et al., 2019; Shaffer, 2021). \nThis study investigates whether and how theoretical linguistics improves language clustering for multilingual named entity recognition (NER). We propose two types of language groupings: one based on morpho-syntactic features in a nominal domain and one based on a head parameter. Our NER experiments show that the proposed methods largely outperform a state-of-the-art embedding-based model, suggesting that theoretical linguistics plays a significant role in multilingual learning tasks.", "anthology_url": "https://aclanthology.org/2023.acl-srw.24", "authors": [ "Sakura Imai", @@ -80915,6 +87363,7 @@ "id": "S50", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.24.pdf", "paper_type": "srw", @@ -80924,14 +87373,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Theoretical Linguistics Rivals Embeddings in Language Clustering for Multilingual Named Entity Recognition", - "tldr": "This study investigates whether and how theoretical linguistics improves language clustering for multilingual named entity recognition (NER), with the two types of language groupings proposed: one based on morpho-syntactic features in a nominal domain and one based on a head parameter....", + "tldr": "While embedding-based methods have been dominant in language clustering for multilingual tasks, clustering based on linguistic features has not yet been explored much, as it remains baselines (Tan et al., 2019; Shaffer, 2021). \nThis study investigates whether and how theoretical linguistics improves...", "track": "Student Research Workshop", "underline_id": 78205, "underline_url": "https://underline.io/events/395/posters/15298/poster/78205-theoretical-linguistics-rivals-embeddings-in-language-clustering-for-multilingual-named-entity-recognition", "video_url": null }, "S52": { - "abstract": "A investage on LLMs", + "abstract": "Chain-of-thought (CoT) prompting, i.e., step-by-step reasoning instruction, enhances the performance of large language models (LLMs) in various tasks. Nevertheless, whether this gain suggests that LLMs have robust reasoning abilities remains unclear. In this study, we inspect the LLMs' step-by-step reasoning ability under controlled settings, with a particular focus on negation. Our results indicate that all tested LLMs were not robust against lexical negation (e.g., plausible -> implausible) when performing the CoT reasoning. Furthermore, our experiments with varying levels revealed that different LLMs exhibit different difficulties and output biases against lexical negation.", "anthology_url": "https://aclanthology.org/2023.acl-srw.25", "authors": [ "Mengyu Ye", @@ -80949,6 +87398,7 @@ "id": "S52", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.25.pdf", "paper_type": "srw", @@ -80958,14 +87408,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Assessing Chain-of-Thought Reasoning against Lexical Negation: A Case Study on Syllogism", - "tldr": "A investage on LLMs...", + "tldr": "Chain-of-thought (CoT) prompting, i.e., step-by-step reasoning instruction, enhances the performance of large language models (LLMs) in various tasks. Nevertheless, whether this gain suggests that LLMs have robust reasoning abilities remains unclear. In this study, we inspect the LLMs' step-by-step ...", "track": "Student Research Workshop", "underline_id": 78192, "underline_url": "https://underline.io/events/395/sessions/15239/lecture/78192-srw-assessing-chain-of-thought-reasoning-against-lexical-negation-a-case-study-on-syllogism", "video_url": null }, "S56": { - "abstract": "A reproduction study of native language prediction from English as second language reading eye-tracking data.", + "abstract": "Numerous studies found that the linguistic properties of a person's native language affect the cognitive processing of other languages. However, only one study has shown that it was possible to identify the native language based on eye-tracking records of natural L2 reading using machine learning. A new corpus allows us to replicate these results on a more interrelated and larger set of native languages. Our results show that comparable classification performance is maintained despite using less data. However, analysis shows that the correlation between L2 eye movements and native language similarity may be more complex than the original study found.", "anthology_url": "https://aclanthology.org/2023.acl-srw.26", "authors": [ "Lina Skerath", @@ -80983,6 +87433,7 @@ "id": "S56", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.26.pdf", "paper_type": "srw", @@ -80992,14 +87443,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Native Language Prediction from Gaze: a Reproducibility Study", - "tldr": "A reproduction study of native language prediction from English as second language reading eye-tracking data....", + "tldr": "Numerous studies found that the linguistic properties of a person's native language affect the cognitive processing of other languages. However, only one study has shown that it was possible to identify the native language based on eye-tracking records of natural L2 reading using machine learning. A...", "track": "Student Research Workshop", "underline_id": 78173, "underline_url": "https://underline.io/events/395/posters/15227/poster/78173-native-language-prediction-from-gaze-a-reproducibility-study", "video_url": null }, "S57": { - "abstract": "We use Prompt-based learning on LLMs for Temporal Classification of Treatment Events from Discharge Summaries of clinical data.", + "abstract": "Discharge summaries are comprehensive medical records that encompass vital information about a patient's hospital stay. A crucial aspect of discharge summaries is the temporal information of treatments administered throughout the patient's illness. \nWith an extensive volume of clinical documents, manually extracting and compiling a patient's medication list can be laborious, time-consuming, and susceptible to errors. \nThe objective of this paper is to build upon the recent development on clinical NLP by temporally classifying treatments in clinical texts, specifically determining whether a treatment was administered between the time of admission and discharge from the hospital.\nState-of-the-art NLP methods including prompt-based learning on Generative Pre-trained Transformers (GPTs) models and fine-tuning on pre-trained language models (PLMs) such as BERT \nwere employed to classify temporal relations between treatments and hospitalisation periods in discharge summaries. \nFine-tuning with the BERT model achieved an F1 score of 92.45\\% and a balanced accuracy of 77.56\\%, while prompt learning using the T5 model and mixed templates resulted in an F1 score of 90.89\\% and a balanced accuracy of 72.07\\%.\nOur codes and data are available at \\url{https://github.com/HECTA-UoM/MedTem}.", "anthology_url": "https://aclanthology.org/2023.acl-srw.27", "authors": [ "Yang Cui", @@ -81015,6 +87466,7 @@ "id": "S57", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.27.pdf", "paper_type": "srw", @@ -81024,14 +87476,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] MedTem2.0: Prompt-based Temporal Classification of Treatment Events from Discharge Summaries", - "tldr": "We use Prompt-based learning on LLMs for Temporal Classification of Treatment Events from Discharge Summaries of clinical data....", + "tldr": "Discharge summaries are comprehensive medical records that encompass vital information about a patient's hospital stay. A crucial aspect of discharge summaries is the temporal information of treatments administered throughout the patient's illness. \nWith an extensive volume of clinical documents, ma...", "track": "Student Research Workshop", "underline_id": 78174, "underline_url": "https://underline.io/events/395/posters/15264/poster/78174-prompt-based-temporal-classification-of-treatment-events-from-discharge-summaries", "video_url": null }, "S58": { - "abstract": "We look at short-term semantic shifts in the Swedish discussion about NATO membership.", + "abstract": "In this paper, we investigate a type of semantic shift that occurs when a sudden event radically changes public opinion on a topic. Looking at Sweden's decision to apply for NATO membership in 2022, we use word embeddings to study how the associations users on Twitter have regarding NATO evolve. We identify several changes that we successfully validate against real-world events. However, the low engagement of the public with the issue often made it challenging to distinguish true signals from noise. We thus find that domain knowledge and data selection are of prime importance when using word embeddings to study semantic shifts.", "anthology_url": "https://aclanthology.org/2023.acl-srw.28", "authors": [ "Brian Bonafilia", @@ -81048,6 +87500,7 @@ "id": "S58", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.28.pdf", "paper_type": "srw", @@ -81057,14 +87510,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Sudden Semantic Shifts in Swedish NATO discourse", - "tldr": "We look at short-term semantic shifts in the Swedish discussion about NATO membership....", + "tldr": "In this paper, we investigate a type of semantic shift that occurs when a sudden event radically changes public opinion on a topic. Looking at Sweden's decision to apply for NATO membership in 2022, we use word embeddings to study how the associations users on Twitter have regarding NATO evolve. We ...", "track": "Student Research Workshop", "underline_id": 78175, "underline_url": "https://underline.io/events/395/posters/15227/poster/78175-sudden-semantic-shifts-in-swedish-nato-discourse", "video_url": null }, "S6": { - "abstract": "The proposed framework incorporates conceptual knowledge for prompt-based text classification in the extreme zero-shot setting, which outperforms existing approaches in sentiment analysis and topic detection on four widely-used datasets.", + "abstract": "In recent years, pre-trained language models have garnered significant attention due to their effectiveness, which stems from the rich knowledge acquired during pre-training. To mitigate the inconsistency issues between pre-training tasks and downstream tasks and to facilitate the resolution of language-related issues, prompt-based approaches have been introduced, which are particularly useful in low-resource scenarios. However, existing approaches mostly rely on verbalizers to translate the predicted vocabulary to task-specific labels. The major limitations of this approach are the ignorance of potentially relevant domain-specific words and being biased by the pre-training data. To address these limitations, we propose a framework that incorporates conceptual knowledge for text classification in the extreme zero-shot setting. The framework includes prompt-based keyword extraction, weight assignment to each prompt keyword, and final representation estimation in the knowledge graph embedding space. We evaluated the method on four widely-used datasets for sentiment analysis and topic detection, demonstrating that it consistently outperforms recently-developed prompt-based approaches in the same experimental settings.", "anthology_url": "https://aclanthology.org/2023.acl-srw.4", "authors": [ "Yuqi Wang", @@ -81083,6 +87536,7 @@ "id": "S6", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.4.pdf", "paper_type": "srw", @@ -81092,14 +87546,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Prompt-based Zero-shot Text Classification with Conceptual Knowledge", - "tldr": "The proposed framework incorporates conceptual knowledge for prompt-based text classification in the extreme zero-shot setting, which outperforms existing approaches in sentiment analysis and topic detection on four widely-used datasets....", + "tldr": "In recent years, pre-trained language models have garnered significant attention due to their effectiveness, which stems from the rich knowledge acquired during pre-training. To mitigate the inconsistency issues between pre-training tasks and downstream tasks and to facilitate the resolution of lang...", "track": "Student Research Workshop", "underline_id": 78311, "underline_url": "https://underline.io/events/395/posters/15200/poster/78311-prompt-based-zero-shot-text-classification-with-conceptual-knowledge", "video_url": null }, "S64": { - "abstract": "This paper presents two types of buzzer-quiz answering systems that can predict the answer from only part of a question and then proposes a method to estimate the accuracy of the answers for each system by using the internal scores of each model.", + "abstract": "A buzzer quiz is a genre of quiz in which multiple players simultaneously listen to a quiz being read aloud and respond it by buzzing in as soon as they can predict the answer.\nBecause incorrect answers often result in penalties, a buzzer-quiz answering system must not only predict the answer from only part of a question but also estimate the predicted answer's accuracy.\nIn this paper, we introduce two types of buzzer-quiz answering systems: (1) a system that directly generates an answer from part of a question by using an autoregressive language model; and (2) a system that first reconstructs the entire question by using an autoregressive language model and then determines the answer according to the reconstructed question.\nWe then propose a method to estimate the accuracy of the answers for each system by using the internal scores of each model.", "anthology_url": "https://aclanthology.org/2023.acl-srw.29", "authors": [ "Naoya Sugiura", @@ -81117,6 +87571,7 @@ "id": "S64", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.29.pdf", "paper_type": "srw", @@ -81126,14 +87581,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Building a Buzzer-quiz Answering System", - "tldr": "This paper presents two types of buzzer-quiz answering systems that can predict the answer from only part of a question and then proposes a method to estimate the accuracy of the answers for each system by using the internal scores of each model....", + "tldr": "A buzzer quiz is a genre of quiz in which multiple players simultaneously listen to a quiz being read aloud and respond it by buzzing in as soon as they can predict the answer.\nBecause incorrect answers often result in penalties, a buzzer-quiz answering system must not only predict the answer from o...", "track": "Student Research Workshop", "underline_id": 78176, "underline_url": "https://underline.io/events/395/posters/15264/poster/78176-building-a-buzzer-quiz-answering-system", "video_url": null }, "S66": { - "abstract": "This paper contributes to hyperbole identification research in NLP with two probing tasks (edge and MDL probing) for 3 pre-trained language models, as well as an attempt to shed light on problems annotating hyperbole.", + "abstract": "Hyperbole is a common figure of speech, which is under-explored in NLP research. In this study, we conduct edge and minimal description length (MDL) probing experiments on three pre-trained language models (PLMs) in an attempt to explore the extent to which hyperbolic information is encoded in these models. We use both word-in-context and sentence-level representations as model inputs as a basis for comparison. We also annotate 63 hyperbole sentences from the HYPO dataset according to an operational taxonomy to conduct an error analysis to explore the encoding of different hyperbole categories. Our results show that hyperbole is to a limited extent encoded in PLMs, and mostly in the final layers. They also indicate that hyperbolic information may be better encoded by the sentence-level representations, which, due to the pragmatic nature of hyperbole, may therefore provide a more accurate and informative representation in PLMs. Finally, the inter-annotator agreement for our annotations, a Cohen's Kappa of 0.339, suggest that the taxonomy categories may not be intuitive and need revision or simplification.", "anthology_url": "https://aclanthology.org/2023.acl-srw.30", "authors": [ "Nina Schneidermann", @@ -81149,6 +87604,7 @@ "id": "S66", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.30.pdf", "paper_type": "srw", @@ -81158,14 +87614,14 @@ "similar_paper_ids": [], "slides_pdf": "https://assets.underline.io/lecture/78220/slideshow/34a2d1c640680ec08fe6c2639f156922.pdf", "title": "[SRW] Probing for Hyperbole in Pre-Trained Language Models", - "tldr": "This paper contributes to hyperbole identification research in NLP with two probing tasks (edge and MDL probing) for 3 pre-trained language models, as well as an attempt to shed light on problems annotating hyperbole....", + "tldr": "Hyperbole is a common figure of speech, which is under-explored in NLP research. In this study, we conduct edge and minimal description length (MDL) probing experiments on three pre-trained language models (PLMs) in an attempt to explore the extent to which hyperbolic information is encoded in these...", "track": "Student Research Workshop", "underline_id": 78220, "underline_url": "https://underline.io/events/395/posters/15254/poster/78220-srw-probing-for-hyperbole-in-pre-trained-language-models", "video_url": null }, "S70": { - "abstract": "This paper is about dialogue act classification and slot tagging in the emergency response domain.", + "abstract": "In this paper we describe the task of adapting NLP models to dialogue processing in the emergency response domain. Our goal is to provide a recipe for building a system that performs dialogue act classification and domain-specific slot tagging while being efficient, flexible and robust. We show that adapter models Pfeiffer et al. (2020) perform well in the emergency response domain and benefit from additional dialogue context and speaker information. Comparing adapters to standard fine-tuned Transformer models we show that they achieve competitive results and can easily accommodate new tasks without significant memory increase since the base model can be shared between the adapters specializing on different tasks. We also address the problem of scarce annotations in the emergency response domain and evaluate different data augmentation techniques in a low-resource setting.", "anthology_url": "https://aclanthology.org/2023.acl-srw.31", "authors": [ "Tatiana Anikina" @@ -81179,6 +87635,7 @@ "id": "S70", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.31.pdf", "paper_type": "srw", @@ -81188,14 +87645,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Towards Efficient Dialogue Processing in the Emergency Response Domain", - "tldr": "This paper is about dialogue act classification and slot tagging in the emergency response domain....", + "tldr": "In this paper we describe the task of adapting NLP models to dialogue processing in the emergency response domain. Our goal is to provide a recipe for building a system that performs dialogue act classification and domain-specific slot tagging while being efficient, flexible and robust. We show that...", "track": "Student Research Workshop", "underline_id": 78314, "underline_url": "https://underline.io/events/395/posters/15240/poster/78314-towards-efficient-dialogue-processing-in-the-emergency-response-domain", "video_url": null }, "S71": { - "abstract": "In the argument clustering for semantic frame induction, we propose a method that applies deep metric learning.", + "abstract": "The semantic frame induction tasks are defined as a clustering of words into the frames that they evoke, and a clustering of their arguments according to the frame element roles that they should fill. In this paper, we address the latter task of argument clustering, which aims to acquire frame element knowledge, and propose a method that applies deep metric learning. In this method, a pre-trained language model is fine-tuned to be suitable for distinguishing frame element roles through the use of frame-annotated data, and argument clustering is performed with embeddings obtained from the fine-tuned model. Experimental results on FrameNet demonstrate that our method achieves substantially better performance than existing methods.", "anthology_url": "https://aclanthology.org/2023.acl-srw.32", "authors": [ "Kosuke Yamada", @@ -81211,6 +87668,7 @@ "id": "S71", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.32.pdf", "paper_type": "srw", @@ -81220,14 +87678,14 @@ "similar_paper_ids": [], "slides_pdf": "https://assets.underline.io/lecture/78193/slideshow/e64a8b60a5655884718f2c648d90af70.pdf", "title": "[SRW] Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction", - "tldr": "In the argument clustering for semantic frame induction, we propose a method that applies deep metric learning....", + "tldr": "The semantic frame induction tasks are defined as a clustering of words into the frames that they evoke, and a clustering of their arguments according to the frame element roles that they should fill. In this paper, we address the latter task of argument clustering, which aims to acquire frame eleme...", "track": "Student Research Workshop", "underline_id": 78193, "underline_url": "https://underline.io/events/395/posters/15254/poster/78193-srw-acquiring-frame-element-knowledge-with-deep-metric-learning-for-semantic-frame-induction", "video_url": null }, "S72": { - "abstract": "This paper propose methods to reduce the number of redundant questions generated by open-domain dialogue systems.", + "abstract": "Neural text generation models have achieved remarkable success in carrying on short open-domain conversations. However, their performance degrades significantly in the long term, especially in their ability to ask coherent questions. A significant issue is the generation of redundant questions where the answer has already been provided by the user. We adapt and evaluate different methods, including negative training, decoding, and classification, to mitigate the redundancy problem. We also propose a simple yet effective method for generating training data without the need for crowdsourcing human-human or human-bot conversations. Experiments with the BlenderBot model show that our combined method significantly reduces the rate of redundant questions from 27.2% to 8.7%, while improving the quality of the original model. The code, dataset, and trained models can be found at our repository.", "anthology_url": "https://aclanthology.org/2023.acl-srw.33", "authors": [ "Long Mai", @@ -81242,6 +87700,7 @@ "id": "S72", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.33.pdf", "paper_type": "srw", @@ -81251,14 +87710,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] I already said that! Degenerating redundant questions in open-domain dialogue systems.", - "tldr": "This paper propose methods to reduce the number of redundant questions generated by open-domain dialogue systems....", + "tldr": "Neural text generation models have achieved remarkable success in carrying on short open-domain conversations. However, their performance degrades significantly in the long term, especially in their ability to ask coherent questions. A significant issue is the generation of redundant questions where...", "track": "Student Research Workshop", "underline_id": 78177, "underline_url": "https://underline.io/events/395/posters/15279/poster/78177-i-already-said-that-degenerating-redundant-questions-in-open-domain-dialogue-systems", "video_url": null }, "S79": { - "abstract": "This paper investigates how humans incorporate speaker-derived information by annotating the utterances in a knowledge-grounded dialogue corpus.", + "abstract": "Currently, most knowledge-grounded dialogue response generation models focus on reflecting given external knowledge. However, even when conveying external knowledge, humans integrate their own knowledge, experiences, and opinions with external knowledge to make their utterances engaging. In this study, we analyze such human behavior by annotating the utterances in an existing knowledge-grounded dialogue corpus. Each entity in the corpus is annotated with its information source, either derived from external knowledge (database-derived) or the speaker's own knowledge, experiences, and opinions (speaker-derived). Our analysis shows that the presence of speaker-derived information in the utterance improves dialogue engagingness. We also confirm that responses generated by an existing model, which is trained to reflect the given knowledge, cannot include speaker-derived information in responses as often as humans do.", "anthology_url": "https://aclanthology.org/2023.acl-srw.34", "authors": [ "Takashi Kodama", @@ -81276,6 +87735,7 @@ "id": "S79", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.34.pdf", "paper_type": "srw", @@ -81285,14 +87745,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Is a Knowledge-based Response Engaging?: An Analysis on Knowledge-Grounded Dialogue with Information Source Annotation", - "tldr": "This paper investigates how humans incorporate speaker-derived information by annotating the utterances in a knowledge-grounded dialogue corpus....", + "tldr": "Currently, most knowledge-grounded dialogue response generation models focus on reflecting given external knowledge. However, even when conveying external knowledge, humans integrate their own knowledge, experiences, and opinions with external knowledge to make their utterances engaging. In this stu...", "track": "Student Research Workshop", "underline_id": 78206, "underline_url": "https://underline.io/events/395/sessions/15239/lecture/78206-srw-is-a-knowledge-based-response-engagingquestion-an-analysis-on-knowledge-grounded-dialogue-with-information-source-annotation", "video_url": null }, "S82": { - "abstract": "More informed masking in cross-lingual visual pre-training for multimodal machine translation.", + "abstract": "Pre-trained language models have achieved remarkable results on several NLP tasks. Most of them adopt masked language modeling to learn representations by randomly masking tokens and predicting them based on their context. However, this random selection of tokens to be masked is inefficient to learn some language patterns as it may not consider linguistic information that can be helpful for many NLP tasks, such as multimodal machine translation (MMT). Hence, we propose three novel masking strategies for cross-lingual visual pre-training - more informed visual masking, more informed textual masking, and more informed visual and textual masking - each one focusing on learning different linguistic patterns. We apply them to Vision Translation Language Modelling for video subtitles (Sato et al., 2022) and conduct extensive experiments on the Portuguese-English MMT task. The results show that our masking approaches yield significant improvements over the original random masking strategy for downstream MMT performance. Our models outperform the MMT baseline and we achieve state-of-the-art accuracy (52.70 in terms of BLEU score) on the How2 dataset, indicating that more informed masking helps in acquiring an understanding of specific language structures and has great potential for language understanding.", "anthology_url": "https://aclanthology.org/2023.acl-srw.35", "authors": [ "Julia Sato", @@ -81308,6 +87768,7 @@ "id": "S82", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.35.pdf", "paper_type": "srw", @@ -81317,14 +87778,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Choosing What to Mask: More Informed Masking for Multimodal Machine Translation", - "tldr": "More informed masking in cross-lingual visual pre-training for multimodal machine translation....", + "tldr": "Pre-trained language models have achieved remarkable results on several NLP tasks. Most of them adopt masked language modeling to learn representations by randomly masking tokens and predicting them based on their context. However, this random selection of tokens to be masked is inefficient to learn...", "track": "Student Research Workshop", "underline_id": 78221, "underline_url": "https://underline.io/events/395/posters/15227/poster/78221-choosing-what-to-mask-more-informed-masking-for-multimodal-machine-translation", "video_url": null }, "S92": { - "abstract": "This paper studies various methods and their effects on multimodal procedural knowledge understanding of injecting the early shallow017 event representations to nowadays multimodal deep learning-based models.", + "abstract": "Procedural knowledge understanding (PKU) underlies the ability to infer goal-step relations. The task of Visual Goal--Step Inference addresses this ability in the multimodal domain. It requires to identify images that represent the steps towards achieving a textually expressed goal. The best existing methods encode texts and images either with independent encoders, or with object-level multimodal encoders using blackbox transformers. This stands in contrast to early, linguistically inspired methods for event representations, which focus on capturing the most crucial information, namely actions and the participants, to learn stereotypical event sequences and hence procedural knowledge. In this work, we study various methods and their effects on PKU of injecting the early shallow event representations to nowadays multimodal deep learning-based models. We find that the early, linguistically inspired methods for representing event knowledge does contribute to understand procedures in combination with modern vision-and-language models. In the future, we are going to explore more complex structure of events and study how to exploit it on top of large language models.", "anthology_url": "https://aclanthology.org/2023.acl-srw.36", "authors": [ "Chong Shen", @@ -81339,6 +87800,7 @@ "id": "S92", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.36.pdf", "paper_type": "srw", @@ -81348,14 +87810,14 @@ "similar_paper_ids": [], "slides_pdf": "https://assets.underline.io/lecture/78222/slideshow/c7854be34c349ad872286f22b5b62b9c.pdf", "title": "[SRW] Combining Tradition with Modernness: Exploring Event Representations in Vision-and-Language Models for Visual Goal-Step Inference", - "tldr": "This paper studies various methods and their effects on multimodal procedural knowledge understanding of injecting the early shallow017 event representations to nowadays multimodal deep learning-based models....", + "tldr": "Procedural knowledge understanding (PKU) underlies the ability to infer goal-step relations. The task of Visual Goal--Step Inference addresses this ability in the multimodal domain. It requires to identify images that represent the steps towards achieving a textually expressed goal. The best existin...", "track": "Student Research Workshop", "underline_id": 78222, "underline_url": "https://underline.io/events/395/posters/15240/poster/78222-combining-tradition-with-modernness-exploring-event-representations-in-vision-and-language-models-for-visual-goal-step-inference", "video_url": null }, "S94": { - "abstract": "This paper proposes a sampling chain based method to make Shapley values computationally feasible for data valuation and selection for large language models.", + "abstract": "Although Shapley values have been shown to be highly effective for identifying harmful training instances, dataset size and model complexity constraints limit the ability to apply Shapley-based data valuation to fine-tuning large pre-trained language models. To address this, we propose TS-DShapley, an algorithm that reduces computational cost of Shapley-based data valuation through: 1) an efficient sampling-based method that aggregates Shapley values computed from subsets for valuation of the entire training set, and 2) a value transfer method that leverages value information extracted from a simple classifier trained using representations from the target language model. Our experiments applying TS-DShapley to select data for fine-tuning BERT-based language models on benchmark natural language understanding (NLU) datasets show that TS-DShapley outperforms existing data selection methods. Further, TS-DShapley can filter fine-tuning data to increase language model performance compared to training with the full fine-tuning dataset.", "anthology_url": "https://aclanthology.org/2023.acl-srw.37", "authors": [ "Stephanie Schoch", @@ -81371,6 +87833,7 @@ "id": "S94", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.37.pdf", "paper_type": "srw", @@ -81380,14 +87843,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Data Selection for Fine-tuning Large Language Models Using Transferred Shapley Values", - "tldr": "This paper proposes a sampling chain based method to make Shapley values computationally feasible for data valuation and selection for large language models....", + "tldr": "Although Shapley values have been shown to be highly effective for identifying harmful training instances, dataset size and model complexity constraints limit the ability to apply Shapley-based data valuation to fine-tuning large pre-trained language models. To address this, we propose TS-DShapley, ...", "track": "Student Research Workshop", "underline_id": 78223, "underline_url": "https://underline.io/events/395/posters/15279/poster/78223-data-selection-for-fine-tuning-large-language-models-using-transferred-shapley-values", "video_url": null }, "S95": { - "abstract": "We define three types of questions (grammar, function word, and context) for fill-in-the-blank exercises and propose a method to generate distractors according to the characteristics of each question type.", + "abstract": "This study addresses the automatic generation of distractors for English fill-in-the-blank exercises in the entrance examinations for Japanese universities. While previous studies applied the same method to all questions, actual entrance examinations have multiple question types that reflect the purpose of the questions. Therefore, we define three types of questions (grammar, function word, and context) and propose a method to generate distractors according to the characteristics of each question type. Experimental results on 500 actual questions show the effectiveness of the proposed method for both automatic and manual evaluation.", "anthology_url": "https://aclanthology.org/2023.acl-srw.38", "authors": [ "Nana Yoshimi", @@ -81405,6 +87868,7 @@ "id": "S95", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.38.pdf", "paper_type": "srw", @@ -81414,18 +87878,17 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Distractor Generation for Fill-in-the-Blank Exercises by Question Type", - "tldr": "We define three types of questions (grammar, function word, and context) for fill-in-the-blank exercises and propose a method to generate distractors according to the characteristics of each question type....", + "tldr": "This study addresses the automatic generation of distractors for English fill-in-the-blank exercises in the entrance examinations for Japanese universities. While previous studies applied the same method to all questions, actual entrance examinations have multiple question types that reflect the pur...", "track": "Student Research Workshop", "underline_id": 78315, "underline_url": "https://underline.io/events/395/posters/15200/poster/78315-distractor-generation-for-fill-in-the-blank-exercises-by-question-type", "video_url": null }, "S98": { - "abstract": "An open-source, user-friendly platform for the continual evaluation and development of models for the Winograd Schema Challenge, based on iterations of human-adversarial perturbations.", + "abstract": "Transformer-based language models have been recently showcasing impressive performance on a number of common-sense reasoning tasks such as the Winograd Schema Challenge (WSC) while continuing to struggle on task instances that are either slightly re-worded or perturbed. In the following paper, we wish to address these issues by re-framing the WSC using a never-ending learning, human-in-the-loop scenario devised specifically for perturbed pronoun co-reference resolution problems. We introduce EvoGrad, an open-source, user-friendly platform for the continual evaluation and development of models, based on iterations of human-adversarial perturbations. Given that common-sense knowledge varies cross-culturally and through time, our platform allows for the communal contribution towards an evolving task that is both inclusive and accessible to wider societies. In addition, we propose a novel mechanism to develop new task instances, and define a new metric to measure model stability on such dynamic tasks, called the Minimum Error Depth. We show that models fine-tuned on a small iteration of EvoGrad have their performance boosted on WSC-based tasks; this indicates a promising synergy between the acquisition of common sense and the never-ending learning paradigm.", "anthology_url": "https://aclanthology.org/2023.acl-srw.39", "authors": [ "Jing Han Sun", - "Jad Kabbara", "Ali Emami" ], "category": "SRW", @@ -81437,6 +87900,7 @@ "id": "S98", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.39.pdf", "paper_type": "srw", @@ -81446,14 +87910,14 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] EvoGrad: An Online Platform for an Evolving Winograd Schema Challenge using Adversarial Human Perturbations", - "tldr": "An open-source, user-friendly platform for the continual evaluation and development of models for the Winograd Schema Challenge, based on iterations of human-adversarial perturbations....", + "tldr": "Transformer-based language models have been recently showcasing impressive performance on a number of common-sense reasoning tasks such as the Winograd Schema Challenge (WSC) while continuing to struggle on task instances that are either slightly re-worded or perturbed. In the following paper, we wi...", "track": "Student Research Workshop", "underline_id": 78224, "underline_url": "https://underline.io/events/395/posters/15298/poster/78224-evograd-an-online-platform-for-an-evolving-winograd-schema-challenge-using-adversarial-human-perturbations", "video_url": null }, "S99": { - "abstract": "This paper studies whether LLMs moral preferences based on prompted political ideology replicate known results obtained in social science studies, using tools from Moral Foundations Theory", + "abstract": "Large Language Models (LLMs) have demonstrated impressive capabilities in generating fluent text, as well as tendencies to reproduce undesirable social biases. This work investigates whether LLMs reproduce the moral biases associated with political groups in the United States, an instance of a broader capability herein termed moral mimicry. This work explores this hypothesis in the GPT-3/3.5 and OPT families of Transformer-based LLMs. Using tools from Moral Foundations Theory, this work shows that these LLMs are indeed moral mimics. When prompted with a liberal or conservative political identity, the models generate text reflecting corresponding moral biases. This study also explores the relationship between moral mimicry and model size, and similarity between human and LLM moral word use.", "anthology_url": "https://aclanthology.org/2023.acl-srw.40", "authors": [ "Gabriel Simmons" @@ -81467,6 +87931,7 @@ "id": "S99", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": "https://aclanthology.org/2023.acl-srw.40.pdf", "paper_type": "srw", @@ -81476,7 +87941,7 @@ "similar_paper_ids": [], "slides_pdf": null, "title": "[SRW] Moral Mimicry: Large Language Models Produce Moral Rationalizations Tailored to Political Identity", - "tldr": "This paper studies whether LLMs moral preferences based on prompted political ideology replicate known results obtained in social science studies, using tools from Moral Foundations Theory...", + "tldr": "Large Language Models (LLMs) have demonstrated impressive capabilities in generating fluent text, as well as tendencies to reproduce undesirable social biases. This work investigates whether LLMs reproduce the moral biases associated with political groups in the United States, an instance of a broad...", "track": "Student Research Workshop", "underline_id": 78225, "underline_url": "https://underline.io/events/395/posters/15240/poster/78225-moral-mimicry-large-language-models-produce-moral-rationalizations-tailored-to-political-identity", @@ -81499,6 +87964,7 @@ "id": "SICon_11", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long (novel;", @@ -81531,6 +87997,7 @@ "id": "SICon_13", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short (novel;", @@ -81570,6 +88037,7 @@ "id": "SICon_15", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long (novel;", @@ -81602,6 +88070,7 @@ "id": "SICon_4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short (novel;", @@ -81638,6 +88107,7 @@ "id": "SICon_5", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long (novel;", @@ -81671,6 +88141,7 @@ "id": "SICon_7", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long (novel;", @@ -81702,6 +88173,7 @@ "id": "SICon_8", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long (novel;", @@ -81736,6 +88208,7 @@ "id": "SemEval_1", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -81770,6 +88243,7 @@ "id": "SemEval_101", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -81805,6 +88279,7 @@ "id": "SemEval_102", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -81836,6 +88311,7 @@ "id": "SemEval_103", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -81868,6 +88344,7 @@ "id": "SemEval_104", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -81902,6 +88379,7 @@ "id": "SemEval_105", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -81939,6 +88417,7 @@ "id": "SemEval_106", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -81971,6 +88450,7 @@ "id": "SemEval_107", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -82004,6 +88484,7 @@ "id": "SemEval_108", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -82038,6 +88519,7 @@ "id": "SemEval_109", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -82068,6 +88550,7 @@ "id": "SemEval_11", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -82102,6 +88585,7 @@ "id": "SemEval_110", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -82132,6 +88616,7 @@ "id": "SemEval_111", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -82165,6 +88650,7 @@ "id": "SemEval_112", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -82197,6 +88683,7 @@ "id": "SemEval_113", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -82228,6 +88715,7 @@ "id": "SemEval_114", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -82263,6 +88751,7 @@ "id": "SemEval_115", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -82295,6 +88784,7 @@ "id": "SemEval_116", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -82328,6 +88818,7 @@ "id": "SemEval_117", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -82361,6 +88852,7 @@ "id": "SemEval_118", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -82397,6 +88889,7 @@ "id": "SemEval_119", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -82430,6 +88923,7 @@ "id": "SemEval_12", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -82463,6 +88957,7 @@ "id": "SemEval_120", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -82494,6 +88989,7 @@ "id": "SemEval_121", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -82526,6 +89022,7 @@ "id": "SemEval_122", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -82556,6 +89053,7 @@ "id": "SemEval_125", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -82588,6 +89086,7 @@ "id": "SemEval_126", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -82623,6 +89122,7 @@ "id": "SemEval_127", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -82656,6 +89156,7 @@ "id": "SemEval_128", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -82694,6 +89195,7 @@ "id": "SemEval_129", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -82726,6 +89228,7 @@ "id": "SemEval_13", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -82758,6 +89261,7 @@ "id": "SemEval_130", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -82790,6 +89294,7 @@ "id": "SemEval_131", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -82823,6 +89328,7 @@ "id": "SemEval_132", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -82856,6 +89362,7 @@ "id": "SemEval_133", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -82888,6 +89395,7 @@ "id": "SemEval_134", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -82921,6 +89429,7 @@ "id": "SemEval_135", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -82955,6 +89464,7 @@ "id": "SemEval_136", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -82991,6 +89501,7 @@ "id": "SemEval_137", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -83026,6 +89537,7 @@ "id": "SemEval_138", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -83060,6 +89572,7 @@ "id": "SemEval_139", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -83096,6 +89609,7 @@ "id": "SemEval_14", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -83128,6 +89642,7 @@ "id": "SemEval_141", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -83160,6 +89675,7 @@ "id": "SemEval_142", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 8: Causal medical claim identification and related PICO frame extraction from social media posts", @@ -83191,6 +89707,7 @@ "id": "SemEval_143", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -83222,6 +89739,7 @@ "id": "SemEval_144", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -83256,6 +89774,7 @@ "id": "SemEval_145", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -83289,6 +89808,7 @@ "id": "SemEval_146", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -83320,6 +89840,7 @@ "id": "SemEval_147", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -83352,6 +89873,7 @@ "id": "SemEval_148", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -83383,6 +89905,7 @@ "id": "SemEval_149", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -83414,6 +89937,7 @@ "id": "SemEval_15", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 11: Learning with Disagreements (Le-Wi-Di)", @@ -83449,6 +89973,7 @@ "id": "SemEval_150", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -83481,6 +90006,7 @@ "id": "SemEval_152", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 11: Learning with Disagreements (Le-Wi-Di)", @@ -83514,6 +90040,7 @@ "id": "SemEval_153", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -83545,6 +90072,7 @@ "id": "SemEval_154", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -83578,6 +90106,7 @@ "id": "SemEval_155", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -83609,6 +90138,7 @@ "id": "SemEval_156", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -83641,6 +90171,7 @@ "id": "SemEval_157", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -83673,6 +90204,7 @@ "id": "SemEval_158", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 11: Learning with Disagreements (Le-Wi-Di)", @@ -83706,6 +90238,7 @@ "id": "SemEval_159", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -83741,6 +90274,7 @@ "id": "SemEval_16", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -83773,6 +90307,7 @@ "id": "SemEval_160", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -83803,6 +90338,7 @@ "id": "SemEval_161", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -83837,6 +90373,7 @@ "id": "SemEval_162", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 11: Learning with Disagreements (Le-Wi-Di)", @@ -83868,6 +90405,7 @@ "id": "SemEval_163", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -83900,6 +90438,7 @@ "id": "SemEval_164", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -83930,6 +90469,7 @@ "id": "SemEval_165", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 11: Learning with Disagreements (Le-Wi-Di)", @@ -83962,6 +90502,7 @@ "id": "SemEval_166", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -83995,6 +90536,7 @@ "id": "SemEval_167", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -84027,6 +90569,7 @@ "id": "SemEval_168", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -84057,6 +90600,7 @@ "id": "SemEval_169", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -84089,6 +90633,7 @@ "id": "SemEval_17", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -84123,6 +90668,7 @@ "id": "SemEval_170", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -84157,6 +90703,7 @@ "id": "SemEval_171", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -84189,6 +90736,7 @@ "id": "SemEval_172", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -84220,6 +90768,7 @@ "id": "SemEval_174", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -84250,6 +90799,7 @@ "id": "SemEval_175", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -84282,6 +90832,7 @@ "id": "SemEval_176", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -84315,6 +90866,7 @@ "id": "SemEval_177", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -84348,6 +90900,7 @@ "id": "SemEval_178", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -84383,6 +90936,7 @@ "id": "SemEval_179", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -84419,6 +90973,7 @@ "id": "SemEval_18", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -84450,6 +91005,7 @@ "id": "SemEval_180", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -84483,6 +91039,7 @@ "id": "SemEval_181", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -84514,6 +91071,7 @@ "id": "SemEval_182", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -84547,6 +91105,7 @@ "id": "SemEval_183", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -84583,6 +91142,7 @@ "id": "SemEval_184", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -84614,6 +91174,7 @@ "id": "SemEval_185", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -84645,6 +91206,7 @@ "id": "SemEval_186", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -84677,6 +91239,7 @@ "id": "SemEval_187", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -84710,6 +91273,7 @@ "id": "SemEval_188", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -84742,6 +91306,7 @@ "id": "SemEval_189", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -84775,6 +91340,7 @@ "id": "SemEval_19", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -84809,6 +91375,7 @@ "id": "SemEval_190", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -84842,6 +91409,7 @@ "id": "SemEval_191", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -84875,6 +91443,7 @@ "id": "SemEval_192", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -84914,6 +91483,7 @@ "id": "SemEval_193", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -84947,6 +91517,7 @@ "id": "SemEval_194", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -84979,6 +91550,7 @@ "id": "SemEval_195", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -85012,6 +91584,7 @@ "id": "SemEval_196", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -85044,6 +91617,7 @@ "id": "SemEval_197", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -85082,6 +91656,7 @@ "id": "SemEval_198", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -85113,6 +91688,7 @@ "id": "SemEval_199", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -85143,6 +91719,7 @@ "id": "SemEval_20", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -85182,6 +91759,7 @@ "id": "SemEval_200", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -85212,6 +91790,7 @@ "id": "SemEval_201", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -85242,6 +91821,7 @@ "id": "SemEval_202", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -85277,6 +91857,7 @@ "id": "SemEval_203", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 11: Learning with Disagreements (Le-Wi-Di)", @@ -85311,6 +91892,7 @@ "id": "SemEval_204", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -85344,6 +91926,7 @@ "id": "SemEval_205", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -85375,6 +91958,7 @@ "id": "SemEval_206", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -85406,6 +91990,7 @@ "id": "SemEval_207", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -85438,6 +92023,7 @@ "id": "SemEval_208", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -85471,6 +92057,7 @@ "id": "SemEval_209", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -85503,6 +92090,7 @@ "id": "SemEval_21", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -85536,6 +92124,7 @@ "id": "SemEval_210", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -85567,6 +92156,7 @@ "id": "SemEval_211", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -85603,6 +92193,7 @@ "id": "SemEval_212", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -85636,6 +92227,7 @@ "id": "SemEval_213", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -85670,6 +92262,7 @@ "id": "SemEval_214", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -85700,6 +92293,7 @@ "id": "SemEval_215", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -85732,6 +92326,7 @@ "id": "SemEval_216", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -85764,6 +92359,7 @@ "id": "SemEval_217", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -85797,6 +92393,7 @@ "id": "SemEval_218", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -85831,6 +92428,7 @@ "id": "SemEval_219", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -85862,6 +92460,7 @@ "id": "SemEval_22", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -85893,6 +92492,7 @@ "id": "SemEval_220", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -85925,6 +92525,7 @@ "id": "SemEval_221", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -85958,6 +92559,7 @@ "id": "SemEval_222", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -85989,6 +92591,7 @@ "id": "SemEval_223", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -86021,6 +92624,7 @@ "id": "SemEval_226", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -86056,6 +92660,7 @@ "id": "SemEval_227", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -86089,6 +92694,7 @@ "id": "SemEval_228", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -86122,6 +92728,7 @@ "id": "SemEval_229", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -86153,6 +92760,7 @@ "id": "SemEval_23", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -86183,6 +92791,7 @@ "id": "SemEval_230", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -86214,6 +92823,7 @@ "id": "SemEval_231", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -86245,6 +92855,7 @@ "id": "SemEval_232", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -86276,6 +92887,7 @@ "id": "SemEval_233", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -86312,6 +92924,7 @@ "id": "SemEval_234", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -86343,6 +92956,7 @@ "id": "SemEval_235", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -86373,6 +92987,7 @@ "id": "SemEval_236", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -86407,6 +93022,7 @@ "id": "SemEval_237", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -86440,6 +93056,7 @@ "id": "SemEval_238", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -86472,6 +93089,7 @@ "id": "SemEval_239", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -86502,6 +93120,7 @@ "id": "SemEval_24", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -86533,6 +93152,7 @@ "id": "SemEval_240", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -86572,6 +93192,7 @@ "id": "SemEval_241", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -86603,6 +93224,7 @@ "id": "SemEval_242", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -86635,6 +93257,7 @@ "id": "SemEval_244", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -86666,6 +93289,7 @@ "id": "SemEval_245", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -86697,6 +93321,7 @@ "id": "SemEval_246", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -86728,6 +93353,7 @@ "id": "SemEval_247", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -86760,6 +93386,7 @@ "id": "SemEval_248", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -86792,6 +93419,7 @@ "id": "SemEval_249", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -86822,6 +93450,7 @@ "id": "SemEval_25", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -86854,6 +93483,7 @@ "id": "SemEval_250", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -86886,6 +93516,7 @@ "id": "SemEval_251", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -86921,6 +93552,7 @@ "id": "SemEval_252", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 11: Learning with Disagreements (Le-Wi-Di)", @@ -86952,6 +93584,7 @@ "id": "SemEval_255", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -86986,6 +93619,7 @@ "id": "SemEval_256", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -87022,6 +93656,7 @@ "id": "SemEval_257", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -87055,6 +93690,7 @@ "id": "SemEval_258", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -87085,6 +93721,7 @@ "id": "SemEval_26", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -87116,6 +93753,7 @@ "id": "SemEval_260", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -87151,6 +93789,7 @@ "id": "SemEval_261", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -87183,6 +93822,7 @@ "id": "SemEval_262", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -87215,6 +93855,7 @@ "id": "SemEval_263", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -87249,6 +93890,7 @@ "id": "SemEval_264", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -87282,6 +93924,7 @@ "id": "SemEval_265", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -87315,6 +93958,7 @@ "id": "SemEval_266", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -87354,6 +93998,7 @@ "id": "SemEval_267", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -87384,6 +94029,7 @@ "id": "SemEval_268", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -87423,6 +94069,7 @@ "id": "SemEval_269", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -87453,6 +94100,7 @@ "id": "SemEval_27", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 11: Learning with Disagreements (Le-Wi-Di)", @@ -87486,6 +94134,7 @@ "id": "SemEval_270", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 8: Causal medical claim identification and related PICO frame extraction from social media posts", @@ -87517,6 +94166,7 @@ "id": "SemEval_271", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -87552,6 +94202,7 @@ "id": "SemEval_272", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -87583,6 +94234,7 @@ "id": "SemEval_273", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -87617,6 +94269,7 @@ "id": "SemEval_274", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -87651,6 +94304,7 @@ "id": "SemEval_275", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -87683,6 +94337,7 @@ "id": "SemEval_276", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -87715,6 +94370,7 @@ "id": "SemEval_277", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -87746,6 +94402,7 @@ "id": "SemEval_278", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -87780,6 +94437,7 @@ "id": "SemEval_279", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -87810,6 +94468,7 @@ "id": "SemEval_28", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -87843,6 +94502,7 @@ "id": "SemEval_281", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -87874,6 +94534,7 @@ "id": "SemEval_282", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -87906,6 +94567,7 @@ "id": "SemEval_284", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -87940,6 +94602,7 @@ "id": "SemEval_285", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -87971,6 +94634,7 @@ "id": "SemEval_286", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -88002,6 +94666,7 @@ "id": "SemEval_287", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -88036,6 +94701,7 @@ "id": "SemEval_288", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -88068,6 +94734,7 @@ "id": "SemEval_289", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -88098,6 +94765,7 @@ "id": "SemEval_29", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -88129,6 +94797,7 @@ "id": "SemEval_290", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -88161,6 +94830,7 @@ "id": "SemEval_291", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -88192,6 +94862,7 @@ "id": "SemEval_292", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -88226,6 +94897,7 @@ "id": "SemEval_293", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -88261,6 +94933,7 @@ "id": "SemEval_294", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -88297,6 +94970,7 @@ "id": "SemEval_295", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -88327,6 +95001,7 @@ "id": "SemEval_296", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -88358,6 +95033,7 @@ "id": "SemEval_297", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -88389,6 +95065,7 @@ "id": "SemEval_298", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 11: Learning with Disagreements (Le-Wi-Di)", @@ -88425,6 +95102,7 @@ "id": "SemEval_299", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -88456,6 +95134,7 @@ "id": "SemEval_30", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -88488,6 +95167,7 @@ "id": "SemEval_300", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -88524,6 +95204,7 @@ "id": "SemEval_301", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -88557,6 +95238,7 @@ "id": "SemEval_302", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -88596,6 +95278,7 @@ "id": "SemEval_303", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -88628,6 +95311,7 @@ "id": "SemEval_305", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -88659,6 +95343,7 @@ "id": "SemEval_306", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -88698,6 +95383,7 @@ "id": "SemEval_307", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -88732,6 +95418,7 @@ "id": "SemEval_308", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -88766,6 +95453,7 @@ "id": "SemEval_309", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -88797,6 +95485,7 @@ "id": "SemEval_31", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -88828,6 +95517,7 @@ "id": "SemEval_310", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -88862,6 +95552,7 @@ "id": "SemEval_311", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -88895,6 +95586,7 @@ "id": "SemEval_312", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -88928,6 +95620,7 @@ "id": "SemEval_313", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -88963,6 +95656,7 @@ "id": "SemEval_314", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -88996,6 +95690,7 @@ "id": "SemEval_315", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -89026,6 +95721,7 @@ "id": "SemEval_316", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -89058,6 +95754,7 @@ "id": "SemEval_317", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -89091,6 +95788,7 @@ "id": "SemEval_318", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -89122,6 +95820,7 @@ "id": "SemEval_319", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 8: Causal medical claim identification and related PICO frame extraction from social media posts", @@ -89154,6 +95853,7 @@ "id": "SemEval_32", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -89185,6 +95885,7 @@ "id": "SemEval_320", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -89219,6 +95920,7 @@ "id": "SemEval_321", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 11: Learning with Disagreements (Le-Wi-Di)", @@ -89250,6 +95952,7 @@ "id": "SemEval_322", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 11: Learning with Disagreements (Le-Wi-Di)", @@ -89283,6 +95986,7 @@ "id": "SemEval_323", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 8: Causal medical claim identification and related PICO frame extraction from social media posts", @@ -89314,6 +96018,7 @@ "id": "SemEval_325", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -89347,6 +96052,7 @@ "id": "SemEval_327", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -89383,6 +96089,7 @@ "id": "SemEval_328", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -89416,6 +96123,7 @@ "id": "SemEval_33", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -89451,6 +96159,7 @@ "id": "SemEval_330", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -89483,6 +96192,7 @@ "id": "SemEval_331", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -89515,6 +96225,7 @@ "id": "SemEval_332", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -89547,6 +96258,7 @@ "id": "SemEval_333", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -89577,6 +96289,7 @@ "id": "SemEval_334", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -89610,6 +96323,7 @@ "id": "SemEval_335", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task Overview Papers", @@ -89645,6 +96359,7 @@ "id": "SemEval_336", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -89680,6 +96395,7 @@ "id": "SemEval_338", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task Overview Papers", @@ -89714,6 +96430,7 @@ "id": "SemEval_339", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task Overview Papers", @@ -89746,6 +96463,7 @@ "id": "SemEval_34", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -89782,6 +96500,7 @@ "id": "SemEval_340", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task Overview Papers", @@ -89816,6 +96535,7 @@ "id": "SemEval_341", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task Overview Papers", @@ -89849,6 +96569,7 @@ "id": "SemEval_342", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task Overview Papers", @@ -89883,6 +96604,7 @@ "id": "SemEval_343", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task Overview Papers", @@ -89919,6 +96641,7 @@ "id": "SemEval_344", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task Overview Papers", @@ -89957,6 +96680,7 @@ "id": "SemEval_345", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task Overview Papers", @@ -89996,6 +96720,7 @@ "id": "SemEval_346", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task Overview Papers", @@ -90028,6 +96753,7 @@ "id": "SemEval_347", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -90061,6 +96787,7 @@ "id": "SemEval_348", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task Overview Papers", @@ -90099,6 +96826,7 @@ "id": "SemEval_349", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task Overview Papers", @@ -90129,6 +96857,7 @@ "id": "SemEval_35", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 8: Causal medical claim identification and related PICO frame extraction from social media posts", @@ -90162,6 +96891,7 @@ "id": "SemEval_36", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -90195,6 +96925,7 @@ "id": "SemEval_37", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -90226,6 +96957,7 @@ "id": "SemEval_38", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -90258,6 +96990,7 @@ "id": "SemEval_39", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -90291,6 +97024,7 @@ "id": "SemEval_4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -90324,6 +97058,7 @@ "id": "SemEval_40", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -90359,6 +97094,7 @@ "id": "SemEval_41", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -90394,6 +97130,7 @@ "id": "SemEval_42", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -90424,6 +97161,7 @@ "id": "SemEval_43", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -90456,6 +97194,7 @@ "id": "SemEval_44", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -90489,6 +97228,7 @@ "id": "SemEval_48", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 8: Causal medical claim identification and related PICO frame extraction from social media posts", @@ -90520,6 +97260,7 @@ "id": "SemEval_49", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -90557,6 +97298,7 @@ "id": "SemEval_5", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -90591,6 +97333,7 @@ "id": "SemEval_50", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -90622,6 +97365,7 @@ "id": "SemEval_51", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -90655,6 +97399,7 @@ "id": "SemEval_53", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -90688,6 +97433,7 @@ "id": "SemEval_54", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -90720,6 +97466,7 @@ "id": "SemEval_55", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -90751,6 +97498,7 @@ "id": "SemEval_57", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -90781,6 +97529,7 @@ "id": "SemEval_58", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -90813,6 +97562,7 @@ "id": "SemEval_6", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -90844,6 +97594,7 @@ "id": "SemEval_60", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -90874,6 +97625,7 @@ "id": "SemEval_61", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -90908,6 +97660,7 @@ "id": "SemEval_62", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data", @@ -90945,6 +97698,7 @@ "id": "SemEval_63", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -90976,6 +97730,7 @@ "id": "SemEval_64", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -91007,6 +97762,7 @@ "id": "SemEval_65", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -91038,6 +97794,7 @@ "id": "SemEval_66", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -91071,6 +97828,7 @@ "id": "SemEval_67", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -91102,6 +97860,7 @@ "id": "SemEval_68", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -91136,6 +97895,7 @@ "id": "SemEval_69", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -91169,6 +97929,7 @@ "id": "SemEval_7", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -91200,6 +97961,7 @@ "id": "SemEval_70", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -91231,6 +97993,7 @@ "id": "SemEval_71", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -91264,6 +98027,7 @@ "id": "SemEval_72", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -91295,6 +98059,7 @@ "id": "SemEval_73", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -91328,6 +98093,7 @@ "id": "SemEval_75", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -91359,6 +98125,7 @@ "id": "SemEval_76", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 5: Clickbait Spoiling", @@ -91389,6 +98156,7 @@ "id": "SemEval_77", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -91423,6 +98191,7 @@ "id": "SemEval_78", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -91453,6 +98222,7 @@ "id": "SemEval_79", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -91492,6 +98262,7 @@ "id": "SemEval_8", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -91526,6 +98297,7 @@ "id": "SemEval_80", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -91557,6 +98329,7 @@ "id": "SemEval_81", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 12: AfriSenti-SemEval: Sentiment Analysis for Low-resource African Languages using Twitter Dataset", @@ -91588,6 +98361,7 @@ "id": "SemEval_82", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -91621,6 +98395,7 @@ "id": "SemEval_83", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -91653,6 +98428,7 @@ "id": "SemEval_84", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -91686,6 +98462,7 @@ "id": "SemEval_85", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -91717,6 +98494,7 @@ "id": "SemEval_87", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -91751,6 +98529,7 @@ "id": "SemEval_88", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 11: Learning with Disagreements (Le-Wi-Di)", @@ -91784,6 +98563,7 @@ "id": "SemEval_89", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 2: MultiCoNER II Multilingual Complex Named Entity Recognition", @@ -91815,6 +98595,7 @@ "id": "SemEval_9", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -91848,6 +98629,7 @@ "id": "SemEval_90", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 6: LegalEval: Understanding Legal Texts", @@ -91881,6 +98663,7 @@ "id": "SemEval_91", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -91911,6 +98694,7 @@ "id": "SemEval_92", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 10: Towards Explainable Detection of Online Sexism", @@ -91944,6 +98728,7 @@ "id": "SemEval_93", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 4: ValueEval: Identification of Human Values behind Arguments", @@ -91977,6 +98762,7 @@ "id": "SemEval_94", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -92009,6 +98795,7 @@ "id": "SemEval_95", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -92042,6 +98829,7 @@ "id": "SemEval_96", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task-1 - Visual Word Sense Disambiguation (Visual-WSD)", @@ -92076,6 +98864,7 @@ "id": "SemEval_97", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup", @@ -92107,6 +98896,7 @@ "id": "SemEval_99", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Task 9: Multilingual Tweet Intimacy Analysis", @@ -92138,6 +98928,7 @@ "id": "StarSEM_100", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -92170,6 +98961,7 @@ "id": "StarSEM_101", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -92202,6 +98994,7 @@ "id": "StarSEM_104", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -92235,6 +99028,7 @@ "id": "StarSEM_105", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -92269,6 +99063,7 @@ "id": "StarSEM_106", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -92300,6 +99095,7 @@ "id": "StarSEM_13", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -92332,6 +99128,7 @@ "id": "StarSEM_15", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -92365,6 +99162,7 @@ "id": "StarSEM_2", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -92397,6 +99195,7 @@ "id": "StarSEM_23", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -92429,6 +99228,7 @@ "id": "StarSEM_24", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -92462,6 +99262,7 @@ "id": "StarSEM_25", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -92494,6 +99295,7 @@ "id": "StarSEM_26", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -92528,6 +99330,7 @@ "id": "StarSEM_27", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -92561,6 +99364,7 @@ "id": "StarSEM_28", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -92594,6 +99398,7 @@ "id": "StarSEM_29", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -92626,6 +99431,7 @@ "id": "StarSEM_3", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -92658,6 +99464,7 @@ "id": "StarSEM_31", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -92690,6 +99497,7 @@ "id": "StarSEM_32", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -92723,6 +99531,7 @@ "id": "StarSEM_34", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -92755,6 +99564,7 @@ "id": "StarSEM_37", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -92787,6 +99597,7 @@ "id": "StarSEM_4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -92821,6 +99632,7 @@ "id": "StarSEM_43", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -92852,6 +99664,7 @@ "id": "StarSEM_44", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -92887,6 +99700,7 @@ "id": "StarSEM_45", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -92921,6 +99735,7 @@ "id": "StarSEM_46", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -92952,6 +99767,7 @@ "id": "StarSEM_50", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -92984,6 +99800,7 @@ "id": "StarSEM_54", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93018,6 +99835,7 @@ "id": "StarSEM_64", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93050,6 +99868,7 @@ "id": "StarSEM_67", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93082,6 +99901,7 @@ "id": "StarSEM_68", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93113,6 +99933,7 @@ "id": "StarSEM_69", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -93145,6 +99966,7 @@ "id": "StarSEM_70", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93177,6 +99999,7 @@ "id": "StarSEM_71", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93209,6 +100032,7 @@ "id": "StarSEM_72", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93241,6 +100065,7 @@ "id": "StarSEM_74", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93272,6 +100097,7 @@ "id": "StarSEM_75", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93303,6 +100129,7 @@ "id": "StarSEM_76", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93334,6 +100161,7 @@ "id": "StarSEM_79", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -93367,6 +100195,7 @@ "id": "StarSEM_8", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93399,6 +100228,7 @@ "id": "StarSEM_80", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -93432,6 +100262,7 @@ "id": "StarSEM_81", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93469,6 +100300,7 @@ "id": "StarSEM_83", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93500,6 +100332,7 @@ "id": "StarSEM_85", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -93531,6 +100364,7 @@ "id": "StarSEM_86", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93562,6 +100396,7 @@ "id": "StarSEM_9", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "short", @@ -93593,6 +100428,7 @@ "id": "StarSEM_F1", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93625,6 +100461,7 @@ "id": "StarSEM_F2", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93657,6 +100494,7 @@ "id": "StarSEM_F3", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93690,6 +100528,7 @@ "id": "StarSEM_F4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93723,6 +100562,7 @@ "id": "StarSEM_F5", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93755,6 +100595,7 @@ "id": "StarSEM_F6", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93791,6 +100632,7 @@ "id": "StarSEM_F7", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93823,6 +100665,7 @@ "id": "StarSEM_F8", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -93856,6 +100699,7 @@ "id": "SustaiNLP_10", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short", @@ -93887,6 +100731,7 @@ "id": "SustaiNLP_14", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long", @@ -93919,6 +100764,7 @@ "id": "SustaiNLP_15", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long", @@ -93950,6 +100796,7 @@ "id": "SustaiNLP_18", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long", @@ -93983,6 +100830,7 @@ "id": "SustaiNLP_19", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short", @@ -94018,6 +100866,7 @@ "id": "SustaiNLP_2", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -94051,6 +100900,7 @@ "id": "SustaiNLP_21", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short", @@ -94081,6 +100931,7 @@ "id": "SustaiNLP_23", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short", @@ -94112,6 +100963,7 @@ "id": "SustaiNLP_25", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short", @@ -94144,6 +100996,7 @@ "id": "SustaiNLP_26", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short", @@ -94177,6 +101030,7 @@ "id": "SustaiNLP_27", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long", @@ -94209,6 +101063,7 @@ "id": "SustaiNLP_28", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short", @@ -94241,6 +101096,7 @@ "id": "SustaiNLP_3", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short", @@ -94274,6 +101130,7 @@ "id": "SustaiNLP_33", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short", @@ -94306,6 +101163,7 @@ "id": "SustaiNLP_34", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long", @@ -94338,6 +101196,7 @@ "id": "SustaiNLP_36", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long", @@ -94369,6 +101228,7 @@ "id": "SustaiNLP_38", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short", @@ -94405,6 +101265,7 @@ "id": "SustaiNLP_39", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long", @@ -94438,6 +101299,7 @@ "id": "SustaiNLP_4", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long", @@ -94475,6 +101337,7 @@ "id": "SustaiNLP_41", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long", @@ -94506,6 +101369,7 @@ "id": "SustaiNLP_44", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short", @@ -94536,6 +101400,7 @@ "id": "SustaiNLP_45", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short", @@ -94568,6 +101433,7 @@ "id": "SustaiNLP_5", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long", @@ -94599,6 +101465,7 @@ "id": "SustaiNLP_6", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long", @@ -94630,6 +101497,7 @@ "id": "SustaiNLP_7", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Long", @@ -94662,6 +101530,7 @@ "id": "SustaiNLP_9", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "Short", @@ -94695,6 +101564,7 @@ "id": "T3649", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -94728,6 +101598,7 @@ "id": "T3791", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -94759,6 +101630,7 @@ "id": "T3863", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -94795,6 +101667,7 @@ "id": "T3887", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -94826,6 +101699,7 @@ "id": "T3895", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -94859,6 +101733,7 @@ "id": "T3955", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -94892,6 +101767,7 @@ "id": "T3967", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -94924,6 +101800,7 @@ "id": "T4019", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -94956,6 +101833,7 @@ "id": "T4059", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -94996,6 +101874,7 @@ "id": "T4075", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95032,6 +101911,7 @@ "id": "T4113", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95065,6 +101945,7 @@ "id": "T4139", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95097,6 +101978,7 @@ "id": "T4197", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95135,6 +102017,7 @@ "id": "T4243", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95166,6 +102049,7 @@ "id": "T4253", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95202,6 +102086,7 @@ "id": "T4263", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95237,6 +102122,7 @@ "id": "T4285", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95271,6 +102157,7 @@ "id": "T4291", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95302,6 +102189,7 @@ "id": "T4305", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95334,6 +102222,7 @@ "id": "T4371", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95366,6 +102255,7 @@ "id": "T4401", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95398,6 +102288,7 @@ "id": "T4405", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95431,6 +102322,7 @@ "id": "T4407", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95464,6 +102356,7 @@ "id": "T4419", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95496,6 +102389,7 @@ "id": "T4433", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95528,6 +102422,7 @@ "id": "T4447", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95561,6 +102456,7 @@ "id": "T4475", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95612,6 +102508,7 @@ "id": "T4497", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95646,6 +102543,7 @@ "id": "T4503", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95677,6 +102575,7 @@ "id": "T4519", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95708,6 +102607,7 @@ "id": "T4523", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95741,6 +102641,7 @@ "id": "T4589", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95778,6 +102679,7 @@ "id": "T4591", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95810,6 +102712,7 @@ "id": "T4601", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95849,6 +102752,7 @@ "id": "T4607", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95880,6 +102784,7 @@ "id": "T4637", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95912,6 +102817,7 @@ "id": "T4647", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95943,6 +102849,7 @@ "id": "T4721", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -95981,6 +102888,7 @@ "id": "T4769", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -96017,6 +102925,7 @@ "id": "T4773", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -96051,6 +102960,7 @@ "id": "T4777", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -96086,6 +102996,7 @@ "id": "T4803", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -96116,6 +103027,7 @@ "id": "T4929", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -96151,6 +103063,7 @@ "id": "T5043", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "TACL", @@ -96186,6 +103099,7 @@ "id": "TrustNLP_10", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96218,6 +103132,7 @@ "id": "TrustNLP_11", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96249,6 +103164,7 @@ "id": "TrustNLP_12", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96283,6 +103199,7 @@ "id": "TrustNLP_13", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96314,6 +103231,7 @@ "id": "TrustNLP_16", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96349,6 +103267,7 @@ "id": "TrustNLP_17", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96381,6 +103300,7 @@ "id": "TrustNLP_18", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96414,6 +103334,7 @@ "id": "TrustNLP_2", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96446,6 +103367,7 @@ "id": "TrustNLP_20", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96478,6 +103400,7 @@ "id": "TrustNLP_21", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96510,6 +103433,7 @@ "id": "TrustNLP_22", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96543,6 +103467,7 @@ "id": "TrustNLP_23", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96576,6 +103501,7 @@ "id": "TrustNLP_25", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96608,6 +103534,7 @@ "id": "TrustNLP_26", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96642,6 +103569,7 @@ "id": "TrustNLP_27", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96675,6 +103603,7 @@ "id": "TrustNLP_28", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96708,6 +103637,7 @@ "id": "TrustNLP_29", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96740,6 +103670,7 @@ "id": "TrustNLP_3", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96772,6 +103703,7 @@ "id": "TrustNLP_32", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96811,6 +103743,7 @@ "id": "TrustNLP_35", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96845,6 +103778,7 @@ "id": "TrustNLP_36", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96876,6 +103810,7 @@ "id": "TrustNLP_37", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96907,6 +103842,7 @@ "id": "TrustNLP_38", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96940,6 +103876,7 @@ "id": "TrustNLP_41", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -96970,6 +103907,7 @@ "id": "TrustNLP_42", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97002,6 +103940,7 @@ "id": "TrustNLP_43", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97035,6 +103974,7 @@ "id": "TrustNLP_45", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97072,6 +104012,7 @@ "id": "TrustNLP_46", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97104,6 +104045,7 @@ "id": "TrustNLP_47", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97135,6 +104077,7 @@ "id": "TrustNLP_48", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97167,6 +104110,7 @@ "id": "TrustNLP_49", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97199,6 +104143,7 @@ "id": "TrustNLP_5", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97232,6 +104177,7 @@ "id": "TrustNLP_50", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97267,6 +104213,7 @@ "id": "TrustNLP_51", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97300,6 +104247,7 @@ "id": "TrustNLP_52", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97334,6 +104282,7 @@ "id": "TrustNLP_53", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97366,6 +104315,7 @@ "id": "TrustNLP_54", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97400,6 +104350,7 @@ "id": "TrustNLP_55", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97431,6 +104382,7 @@ "id": "TrustNLP_56", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97465,6 +104417,7 @@ "id": "TrustNLP_57", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97502,6 +104455,7 @@ "id": "TrustNLP_59", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97535,6 +104489,7 @@ "id": "TrustNLP_6", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97568,6 +104523,7 @@ "id": "TrustNLP_60", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97601,6 +104557,7 @@ "id": "TrustNLP_8", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "", @@ -97632,6 +104589,7 @@ "id": "WASSA_1", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -97663,6 +104621,7 @@ "id": "WASSA_10", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -97696,6 +104655,7 @@ "id": "WASSA_101", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -97728,6 +104688,7 @@ "id": "WASSA_102", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -97760,6 +104721,7 @@ "id": "WASSA_103", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -97794,6 +104756,7 @@ "id": "WASSA_104", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -97826,6 +104789,7 @@ "id": "WASSA_105", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -97858,6 +104822,7 @@ "id": "WASSA_106", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -97891,6 +104856,7 @@ "id": "WASSA_107", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -97925,6 +104891,7 @@ "id": "WASSA_108", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -97957,6 +104924,7 @@ "id": "WASSA_109", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -97987,6 +104955,7 @@ "id": "WASSA_11", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98018,6 +104987,7 @@ "id": "WASSA_110", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98052,6 +105022,7 @@ "id": "WASSA_111", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98084,6 +105055,7 @@ "id": "WASSA_112", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98115,6 +105087,7 @@ "id": "WASSA_15", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98146,6 +105119,7 @@ "id": "WASSA_16", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98178,6 +105152,7 @@ "id": "WASSA_17", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98211,6 +105186,7 @@ "id": "WASSA_18", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98244,6 +105220,7 @@ "id": "WASSA_201", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98278,6 +105255,7 @@ "id": "WASSA_202", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98311,6 +105289,7 @@ "id": "WASSA_203", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98341,6 +105320,7 @@ "id": "WASSA_204", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98375,6 +105355,7 @@ "id": "WASSA_205", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98405,6 +105386,7 @@ "id": "WASSA_206", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98440,6 +105422,7 @@ "id": "WASSA_21", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98471,6 +105454,7 @@ "id": "WASSA_22", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98504,6 +105488,7 @@ "id": "WASSA_23", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98537,6 +105522,7 @@ "id": "WASSA_24", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98568,6 +105554,7 @@ "id": "WASSA_27", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98600,6 +105587,7 @@ "id": "WASSA_28", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98632,6 +105620,7 @@ "id": "WASSA_29", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98663,6 +105652,7 @@ "id": "WASSA_30", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98698,6 +105688,7 @@ "id": "WASSA_31", "is_paper": true, "keywords": [], + "languages": [], "material": null, "paper_pdf": null, "paper_type": "long", @@ -98733,6 +105724,7 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experiment|approaches for low-resource settings, +599,Machine Learning for NLP,few-shot learning,approaches for low-resource settings, +600,"Language Grounding to Vision, Robotics, and Beyond",cross-modal pretraining,model analysis & interpretability, +601,NLP Applications,security/privacy,nlp engineering experiment, +604,NLP Applications,knowledge graphs,"nlp engineering experiment|approaches for low-compute settings, efficiency", +605,NLP Applications,knowledge graphs,nlp engineering experiment|model analysis & interpretability, +606,Computational Social Science and Cultural Analytics,psycho-demographic trait prediction,position papers, +608,"Language Grounding to Vision, Robotics, and Beyond",cross-modal pretraining,nlp engineering experiment|publicly available software and pre-trained models, +609,Information Extraction,named entity recognition and relation extraction,model analysis & interpretability, +610,Machine Learning for NLP,data augmentation,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models, +612,Machine Translation,speech translation,nlp engineering experiment|model analysis & interpretability, +613,Information Extraction,named entity recognition and relation extraction|event extraction|zero/few-shot extraction,approaches for low-resource settings|data resources, +614,Resources and Evaluation,nlp datasets,nlp engineering experiment|data resources|data analysis, +618,Speech and Multimodality,,model analysis & interpretability, +619,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment|model analysis & interpretability, +620,Machine Learning for NLP,model compression methods,"approaches for low-compute settings, efficiency", +624,Machine Learning for NLP,representation learning,nlp engineering experiment, +625,NLP Applications,multimodal applications,nlp engineering experiment|approaches for low-resource settings, +626,Resources and Evaluation,corpus creation|language resources|datasets for low resource languages,data resources,swiss german|german +633,Large Language Models,pre-training,model analysis & interpretability|reproduction study, +642,Dialogue and Interactive Systems,task-oriented,nlp engineering experiment|publicly available software and pre-trained models, +645,Large Language Models,prompting,nlp engineering experiment, +647,Discourse and Pragmatics,discourse relations,nlp engineering experiment|publicly available software and pre-trained models, +649,"Language Grounding to Vision, Robotics, and Beyond",vision language navigation|cross-modal application,nlp engineering experiment|approaches for low-resource settings, +650,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training,model analysis & interpretability, +651,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",argument mining,nlp engineering experiment|data resources, +652,Speech and 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generation,nlp engineering experiment, +680,Machine Learning for NLP,parameter-efficient finetuning,"nlp engineering experiment|approaches for low-compute settings, efficiency", +683,NLP Applications,financial/business nlp,data resources, +692,Theme Track: Reality Check,(non-)reproducibility|evaluation|methodology,reproduction study, +693,Multilingualism and Cross-Lingual NLP,cross-lingual transfer,approaches for low-resource settings,chinese|spanish|indonesian +698,Large Language Models,pre-training|retrieval-augmented models,publicly available software and pre-trained models|data resources, +701,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",argument mining,model analysis & interpretability|data analysis, +703,Dialogue and Interactive Systems,human-in-the-loop,nlp engineering experiment|data resources, +705,Dialogue and Interactive Systems,knowledge augmented,approaches for low-resource settings, +706,Dialogue and Interactive Systems,conversational modeling,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings, +707,Resources and Evaluation,corpus creation|language resources|nlp datasets,model analysis & interpretability|approaches for low-resource settings|data resources, +710,NLP Applications,knowledge graphs,nlp engineering experiment|model analysis & interpretability, +711,Information Extraction,named entity recognition and relation extraction|event extraction|multilingual extraction,nlp engineering experiment,chinese|arabic|german|french|finnish +712,Information Retrieval and Text Mining,passage retrieval,nlp engineering experiment, +713,Multilingualism and Cross-Lingual NLP,cross-lingual transfer|multilingual pre-training,model analysis & interpretability|publicly available software and pre-trained models|data resources, +718,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",argument mining,nlp engineering experiment, +720,Generation,multilingualism|text-to-text generation,data resources|data analysis,chinese +726,"Language Grounding to Vision, Robotics, and Beyond",cross-modal application,nlp engineering experiment|data resources, +728,Ethics and NLP,data ethics,data analysis|position papers|theory, +731,Dialogue and Interactive Systems,spoken dialogue systems,nlp engineering experiment|data resources|data analysis, +735,Generation,text-to-text generation,nlp engineering experiment, +736,Resources and Evaluation,automatic creation and evaluation of language resources,approaches for low-resource settings, +738,NLP Applications,knowledge graphs,nlp engineering experiment, +739,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",argument mining,model analysis & interpretability|data resources|data analysis,chinese +745,Machine Learning for NLP,continual learning,nlp engineering experiment, +747,Question Answering,generalization,nlp engineering experiment, +751,Linguistic Diversity,less-resourced languages,nlp engineering experiment|approaches for 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experiment|approaches for low-resource settings, +809,Dialogue and Interactive Systems,task-oriented,nlp engineering experiment, +810,Large Language Models,interpretability/analysis,nlp engineering experiment|approaches for low-resource settings|reproduction study, +813,Machine Learning for NLP,transfer learning / domain adaptation|parameter-efficient finetuning|few-shot learning,"nlp engineering experiment|approaches for low-compute settings, efficiency|approaches for low-resource settings|publicly available software and pre-trained models|data resources",chinese +819,"Language Grounding to Vision, Robotics, and Beyond",cross-modal content generation,publicly available software and pre-trained models, +821,Information Extraction,event extraction,model analysis & interpretability, +823,Multilingualism and Cross-Lingual NLP,multilingualism,approaches for low-resource settings|publicly available software and pre-trained models, +824,Information Extraction,open information extraction,nlp engineering experiment|publicly available software and pre-trained models|data resources, +829,Multilingualism and Cross-Lingual NLP,multilingual pre-training,publicly available software and pre-trained models, +832,Machine Translation,automatic evaluation,model analysis & interpretability,german +833,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +834,Theme Track: Reality Check,evaluation|methodology|ai hype & expectations|science-vs-engineering,reproduction study|position papers, +835,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",paraphrase recognition|textual entailment|natural language inference,nlp engineering experiment, +843,NLP Applications,"healthcare applications, clincial nlp",nlp engineering experiment, +844,Machine Learning for NLP,generative models,nlp engineering experiment|theory, +845,Dialogue and Interactive Systems,task-oriented,nlp engineering experiment|approaches for low-resource settings, +851,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",semantic textual similarity|phrase/sentence embedding,nlp engineering experiment|approaches for low-resource settings|data resources, +854,Machine Learning for NLP,contrastive learning|representation learning,"approaches for low-compute settings, efficiency|approaches for low-resource settings", +855,Summarization,multimodal summarization,nlp engineering experiment, +858,Summarization,abstractive summarisation,nlp engineering experiment, +860,Resources and Evaluation,datasets for low resource languages,data resources,korean +867,Speech and Multimodality,speech technologies,publicly available software and pre-trained models,hokkien|french|russian|spanish +868,Large Language Models,fine-tuning,nlp engineering experiment|model analysis & interpretability, +872,Question Answering,question generation,theory, +882,Machine Learning for NLP,representation learning,"approaches for low-compute settings, 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interpretability|approaches for low-compute settings, efficiency|approaches for low-resource settings|publicly available software and pre-trained models|data resources|data analysis", +908,"Language Grounding to Vision, Robotics, and Beyond",cross-modal content generation|cross-modal application,model analysis & interpretability|approaches for low-resource settings,chinese +909,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",argument generation,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings|publicly available software and pre-trained models|data resources|data analysis, +910,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",semantic textual similarity,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models, +914,NLP Applications,code generation and understanding,nlp engineering experiment, +915,Large Language Models,interpretability/analysis,theory, +921,Linguistic Diversity,less-resourced languages|resources for less-resourced languages,approaches for low-resource settings|data resources,hausa|igbo|nigerian pidgin|yoruba +923,Information Retrieval and Text Mining,passage retrieval,nlp engineering experiment, +927,Interpretability and Analysis of Models for NLP,topic modeling,nlp engineering experiment|model analysis & interpretability|theory, +933,Machine Learning for NLP,multi-task learning,model analysis & interpretability, +938,Multilingualism and Cross-Lingual NLP,cross-lingual transfer,"approaches for low-compute settings, efficiency", +941,Machine Learning for NLP,representation learning,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models|data resources, +942,Ethics and NLP,model bias/fairness evaluation,model analysis & interpretability, +953,Dialogue and Interactive Systems,task-oriented|knowledge augmented|commonsense reasoning|conversational modeling,nlp engineering experiment|publicly available software and pre-trained models, +955,NLP Applications,"healthcare applications, clincial nlp",nlp engineering experiment, +957,Machine Learning for NLP,generative models|graphical models,nlp engineering experiment|theory, +958,Machine Learning for NLP,knowledge-augmented methods|transfer learning / domain adaptation|generalization,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings, +959,Computational Social Science and Cultural Analytics,human behavior analysis|stance detection|emotion detection and analysis|sociolinguistics|nlp tools for social analysis|quantiative analyses of news and/or social media,nlp engineering experiment|data resources, +965,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",textual entailment,model analysis & interpretability, +968,Summarization,abstractive summarisation,nlp engineering experiment|publicly available software and pre-trained models|theory, +970,Summarization,query-focused summarization,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings|publicly available software and pre-trained models, +971,Information Retrieval and Text Mining,passage retrieval|dense retrieval,nlp engineering experiment|approaches for low-resource settings, +976,Resources and Evaluation,nlp datasets,nlp engineering experiment|data resources|data analysis,chinese +982,Information Extraction,event extraction,nlp engineering experiment|approaches for low-resource settings, +985,Question Answering,question generation,nlp engineering experiment, +986,Information Retrieval and Text Mining,document representation,nlp engineering experiment, +987,Large Language Models,prompting|interpretability/analysis|robustness,nlp engineering experiment|model analysis & interpretability, +989,"Syntax: Tagging, Chunking, and Parsing",constituency parsing|grammar and knowledge-based approaches,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency|theory", +991,"Language Grounding to Vision, Robotics, and Beyond",cross-modal application,approaches for low-resource settings, +998,NLP Applications,multimodal applications,nlp engineering experiment,chinese +1012,NLP Applications,security/privacy,nlp engineering experiment, +1015,Machine Learning for NLP,few-shot learning,approaches for low-resource settings, +1018,Generation,inference methods,nlp engineering experiment|model analysis & interpretability|theory, +1021,Resources and Evaluation,corpus creation|benchmarking|evaluation,data resources, +1022,Question Answering,open-domain qa,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings|data analysis, +1027,Resources and Evaluation,nlp datasets,data resources,chinese +1029,Information Extraction,named entity recognition and relation extraction,approaches for low-resource settings, +1033,Dialogue and Interactive Systems,applications|conversational modeling,nlp engineering experiment|approaches for low-resource settings, +1034,Linguistic Diversity,less-resourced languages,data resources,bangla +1035,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +1040,Theme Track: Reality Check,lessons from deployment,"approaches for low-compute settings, efficiency", +1046,NLP Applications,knowledge graphs,"model analysis & interpretability|approaches for low-compute settings, efficiency|approaches for low-resource settings", +1047,Computational Social Science and Cultural Analytics,misinformation detection and analysis,nlp engineering experiment, +1048,Resources and Evaluation,evaluation,nlp engineering experiment|model analysis & interpretability|data resources|data analysis, +1056,Question Answering,few-shot qa,nlp engineering experiment|approaches for low-resource settings, +1057,Interpretability and Analysis of Models for NLP,robustness,model analysis & interpretability, +1059,Machine Translation,online adaptation for mt,nlp engineering experiment|model analysis & interpretability|data resources, +1060,Semantics: Lexical,metaphor,nlp engineering experiment, +1061,"Language Grounding to Vision, Robotics, and Beyond",cross-modal application,nlp engineering experiment|approaches for low-resource settings|reproduction study, +1062,Dialogue and Interactive Systems,task-oriented|multilingual / low resource,approaches for low-resource settings, +1065,Machine Learning for NLP,generalization,model analysis & interpretability, +1067,"Language Grounding to Vision, Robotics, and Beyond",vision question answering,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models|reproduction study, +1069,Question Answering,table qa,nlp engineering experiment|data resources, +1071,Large Language Models,fine-tuning,nlp engineering experiment|approaches for low-resource settings, +1072,Large Language Models,prompting|fine-tuning,"approaches for low-compute settings, efficiency|approaches for low-resource settings|publicly available software and pre-trained models", +1074,Machine Learning for NLP,graph-based methods,nlp engineering experiment|approaches for low-resource settings, +1075,Multilingualism and Cross-Lingual NLP,cross-lingual transfer,approaches for low-resource settings, +1078,Multilingualism and Cross-Lingual NLP,mutlilingual representations,nlp engineering experiment|publicly available software and pre-trained models, +1080,Computational Social Science and Cultural Analytics,stance detection,nlp engineering experiment|approaches for low-resource settings, +1084,Large Language Models,continual learning,"model analysis & interpretability|approaches for low-compute settings, efficiency|approaches for low-resource settings", +1085,Information Extraction,document-level extraction,nlp engineering experiment, +1086,Speech and Multimodality,multimodality,nlp engineering experiment,chinese +1087,Large Language Models,applications,publicly available software and pre-trained models|data resources|reproduction study,chinese +1090,Dialogue and Interactive Systems,knowledge augmented|applications|conversational modeling,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models,chinese +1099,NLP Applications,financial/business nlp,model analysis & interpretability, +1101,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",semantic textual similarity,nlp engineering experiment|model analysis & interpretability, +1102,Dialogue and Interactive Systems,knowledge augmented|grounded dialog,nlp engineering experiment|approaches for low-resource settings, +1108,"Language Grounding to Vision, Robotics, and Beyond",cross-modal pretraining,nlp engineering experiment, +1117,Theme Track: Reality Check,(non-)generalizability|lessons from other fields,position papers, +1122,"Syntax: Tagging, Chunking, and Parsing","low-resources languages pos tagging, parsing and related tasks",approaches for low-resource settings, +1126,Machine Translation,automatic evaluation,nlp engineering experiment|model analysis & interpretability|position papers, +1127,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment, +1129,Summarization,evaluation|factuality,nlp engineering experiment|model analysis & interpretability, +1132,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",argument quality assessment,nlp engineering experiment|data analysis, +1133,Summarization,conversational summarization,approaches for low-resource settings, +1136,"Syntax: Tagging, Chunking, and Parsing",semantic parsing,model analysis & interpretability|data resources|data analysis, +1137,Dialogue and Interactive Systems,spoken dialogue systems,"nlp engineering experiment|approaches for low-compute settings, efficiency|publicly available software and pre-trained models", +1139,"Language Grounding to Vision, Robotics, and Beyond",cross-modal content generation|cross-modal application,model analysis & interpretability, +1142,Semantics: Lexical,polysemy|word embeddings,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency|publicly available software and pre-trained models", +1143,Multilingualism and Cross-Lingual NLP,cross-lingual transfer,approaches for low-resource settings, +1148,Dialogue and Interactive Systems,conversational modeling,"nlp engineering experiment|approaches for low-compute settings, efficiency", +1149,Generation,text-to-text generation,nlp engineering experiment, +1150,Machine Translation,automatic evaluation|mt deployment and maintainence,nlp engineering experiment,german +1154,NLP Applications,code generation and understanding,nlp engineering experiment, 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efficiency|approaches for low-resource settings", +1174,Information Extraction,knowledge base construction,nlp engineering experiment|publicly available software and pre-trained models, +1178,Information Extraction,document-level extraction,nlp engineering experiment, +1180,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment|publicly available software and pre-trained models,german +1183,Dialogue and Interactive Systems,conversational modeling,nlp engineering experiment, +1185,Machine Translation,automatic evaluation,nlp engineering experiment, +1190,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",argument mining,nlp engineering experiment, +1191,Summarization,conversational summarization,nlp engineering experiment|model analysis & interpretability, +1194,NLP Applications,knowledge graphs,publicly available software and pre-trained models, +1195,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training,"model analysis & interpretability|approaches for low-compute settings, efficiency|theory", +1200,Machine Translation,few-shot/zero-shot mt|multilingual mt|multimodality,approaches for low-resource settings,chinese +1205,Resources and Evaluation,benchmarking|language resources|multilingual corpora|nlp datasets,nlp engineering experiment,bulgarian +1206,Dialogue and Interactive Systems,task-oriented|factuality|applications,nlp engineering experiment|model analysis & interpretability|theory, +1207,Large Language Models,prompting|interpretability/analysis,model analysis & interpretability, +1212,Dialogue and Interactive Systems,knowledge augmented|grounded dialog,nlp engineering experiment|data resources,chinese +1213,Dialogue and Interactive Systems,task-oriented|applications,nlp engineering experiment|model analysis & interpretability|theory, +1217,NLP Applications,mathematical nlp,model analysis & interpretability|publicly available software and pre-trained models|data resources, +1222,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",applications,model analysis & interpretability, +1223,Dialogue and Interactive Systems,task-oriented|factuality|applications,nlp engineering experiment|model analysis & interpretability|data resources|position papers, +1226,Dialogue and Interactive Systems,knowledge augmented|applications|conversational modeling,nlp engineering experiment, +1227,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment, +1228,Generation,text-to-text generation,nlp engineering experiment, +1236,Theme Track: Reality Check,(non-)generalizability|evaluation|methodology,model analysis & interpretability|approaches for low-resource settings, +1239,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training|robustness,nlp engineering experiment|model analysis & interpretability|theory, +1242,Resources and Evaluation,nlp datasets,model analysis & 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Extraction,document-level extraction,nlp engineering experiment|approaches for low-resource settings, +1489,Summarization,abstractive summarisation,"approaches for low-compute settings, efficiency|data resources",vietnamese +1491,Machine Translation,modelling,model analysis & interpretability|approaches for low-resource settings, +1495,Generation,efficient models,"approaches for low-compute settings, efficiency", +1498,"Syntax: Tagging, Chunking, and Parsing",dependency parsing,nlp engineering experiment|approaches for low-resource settings|data resources,chinese +1500,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment, +1504,Dialogue and Interactive Systems,human-in-the-loop|grounded dialog|conversational modeling,nlp engineering experiment, +1509,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training,nlp engineering experiment|model analysis & interpretability, +1510,"Sentiment Analysis, Stylistic Analysis, and 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+1594,Machine Translation,parallel decoding/non-autoregressive mt,nlp engineering experiment|model analysis & interpretability, +1595,Information Extraction,open information extraction,nlp engineering experiment|model analysis & interpretability, +1602,Multilingualism and Cross-Lingual NLP,cross-lingual transfer|mutlilingual representations|multilingual pre-training|multilingual benchmarks|multilingual evaluation,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models|data resources, +1605,Dialogue and Interactive Systems,multi-modal dialogue systems,publicly available software and pre-trained models, +1610,Discourse and Pragmatics,discourse relations,nlp engineering experiment, +1620,Machine Learning for NLP,transfer learning / domain adaptation,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models, +1621,"Phonology, Morphology, and Word Segmentation",morphological 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interpretability|approaches for low-resource settings|publicly available software and pre-trained models, +1772,Summarization,abstractive summarisation,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings, +1778,Multilingualism and Cross-Lingual NLP,multilingual pre-training,nlp engineering experiment, +1780,Information Extraction,event extraction,nlp engineering experiment, +1785,Machine Translation,online adaptation for mt,nlp engineering experiment|model analysis & interpretability|data analysis, +1786,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training|robustness,nlp engineering experiment|data analysis, +1793,Computational Social Science and Cultural Analytics,emotion detection and analysis,nlp engineering experiment, +1796,Interpretability and Analysis of Models for NLP,explanation faithfulness|feature attribution|free-text/natural language explanations,model analysis & interpretability, 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NLP,cross-lingual transfer|multilingual benchmarks|multilingual evaluation,model analysis & interpretability|publicly available software and pre-trained models|data resources|data analysis,chinese|japanese +1916,Question Answering,multimodal qa,publicly available software and pre-trained models, +1917,NLP Applications,legal nlp,nlp engineering experiment|data resources|data analysis, +1922,Machine Translation,pre-training for mt,nlp engineering experiment, +1924,Large Language Models,pre-training|prompting,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models, +1925,Question Answering,commonsense qa|knowledge base qa|open-domain qa,"nlp engineering experiment|approaches for low-compute settings, efficiency", +1934,Machine Translation,domain adaptation,nlp engineering experiment|model analysis & interpretability, +1936,Multilingualism and Cross-Lingual NLP,cross-lingual transfer,nlp engineering experiment, +1937,Machine Learning for NLP,transfer learning / domain adaptation|model compression methods,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency|approaches for low-resource settings|publicly available software and pre-trained models|data analysis", +1939,Machine Learning for NLP,generalization,approaches for low-resource settings|data resources, +1940,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",reasoning,nlp engineering experiment|data resources, +1952,Question Answering,reading comprehension,theory,chinese +1956,Resources and Evaluation,nlp datasets,data resources, +1958,Generation,data-to-text generation,nlp engineering experiment, +1959,Large Language Models,prompting|interpretability/analysis|applications,nlp engineering experiment|model analysis & interpretability, +1967,Information Extraction,entity linking/disambiguation,data resources, +1969,Generation,data-to-text generation,data resources,chinese +1970,Question Answering,reading comprehension|multihop qa|table qa,nlp engineering experiment, +1976,Question Answering,reading comprehension,nlp engineering experiment, +1977,"Language Grounding to Vision, Robotics, and Beyond",cross-modal application,nlp engineering experiment, +1980,Large Language Models,prompting|applications,"nlp engineering experiment|approaches for low-compute settings, efficiency|approaches for low-resource settings", +1985,Question Answering,reading comprehension|question generation,nlp engineering experiment, +1996,Interpretability and Analysis of Models for NLP,counterfactual/contrastive explanations,model analysis & interpretability, +2005,Question Answering,table qa,nlp engineering experiment, +2006,Large Language Models,pre-training,nlp engineering experiment, +2011,Resources and Evaluation,language resources|lexicon creation|datasets for low resource languages,nlp engineering experiment|approaches for low-resource settings|data resources|data analysis|reproduction study, +2012,Large Language Models,prompting,nlp engineering experiment, +2014,"Language Grounding to Vision, Robotics, and Beyond",cross-modal application,nlp engineering experiment|data analysis, +2017,Interpretability and Analysis of Models for NLP,robustness,model analysis & interpretability, +2018,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",computational psycholinguistics,reproduction study|theory, +2019,Machine Learning for NLP,reinforcement learning,"nlp engineering experiment|approaches for low-compute settings, efficiency|approaches for low-resource settings", +2021,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",linguistic theories,model analysis & interpretability, +2022,"Syntax: Tagging, Chunking, and Parsing",semantic parsing,nlp engineering experiment, +2023,Semantics: Lexical,compositionality,approaches for low-resource settings, +2024,Ethics and NLP,model bias/fairness evaluation,nlp engineering experiment, +2027,Large Language Models,prompting,approaches for low-resource settings, +2032,NLP Applications,knowledge graphs,nlp engineering experiment|approaches for low-resource settings, +2033,Theme Track: Reality Check,methodology,position papers|surveys, +2036,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment, +2037,NLP Applications,"fact checking, rumour/misinformation detection",nlp engineering experiment|model analysis & interpretability, +2039,Ethics and NLP,model bias/fairness evaluation,nlp engineering experiment|model analysis & interpretability|data resources,spanish|german|japanese +2042,Information Extraction,named entity recognition and relation extraction,approaches for low-resource settings, +2046,Question Answering,reasoning|math qa,nlp engineering experiment|approaches for low-resource settings, +2048,Machine Learning for NLP,multi-task learning,nlp engineering experiment, +2052,Generation,text-to-text generation|retrieval-augmented generation,approaches for low-resource settings|data resources,chinese +2055,Generation,automatic evaluation,model analysis & interpretability|data analysis, +2056,Dialogue and Interactive Systems,multi-modal dialogue systems,nlp engineering experiment, +2057,Dialogue and Interactive Systems,applications|grounded dialog|conversational modeling,nlp engineering experiment, +2058,Interpretability and Analysis of Models for NLP,hardness of samples,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency|approaches for low-resource settings", +2059,Machine Translation,speech translation,nlp engineering experiment|approaches for low-resource settings, +2061,NLP Applications,financial/business nlp,nlp engineering experiment|approaches for low-resource settings|data analysis, +2067,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +2068,Summarization,abstractive summarisation|architectures,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models, +2071,Machine Translation,domain adaptation,nlp engineering experiment, +2079,Resources and Evaluation,evaluation,approaches for low-resource settings,german|french|turkish|quechua +2080,Machine Learning for NLP,contrastive learning,"nlp engineering experiment|approaches for low-compute settings, efficiency", +2086,Large Language Models,prompting,nlp engineering experiment, +2091,Dialogue and Interactive Systems,bias/toxicity,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models|theory, +2092,NLP Applications,"healthcare applications, clincial nlp",nlp engineering experiment|model analysis & interpretability, +2093,Question Answering,question generation,nlp engineering experiment, +2095,Semantics: Lexical,word embeddings,"approaches for low-compute settings, efficiency", +2097,Dialogue and Interactive Systems,spoken dialogue systems|task-oriented,"approaches for low-compute settings, efficiency", +2098,Machine Learning for NLP,few-shot learning,approaches for low-resource settings, +2105,Information Retrieval and Text Mining,document representation,"nlp engineering experiment|approaches for low-compute settings, efficiency|publicly available software and pre-trained models|data analysis", +2106,Multilingualism and Cross-Lingual NLP,cross-lingual transfer,nlp engineering experiment, +2112,Semantics: Lexical,word embeddings,nlp engineering experiment, +2114,Semantics: Lexical,lexical relationships,nlp engineering experiment|data resources, +2116,Semantics: Lexical,metaphor,model analysis & interpretability, +2119,Question Answering,knowledge base qa,model analysis & interpretability|data resources, +2122,Machine Learning for NLP,generative models,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models, +2123,"Language 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+2140,NLP Applications,multimodal applications,model analysis & interpretability|data resources, +2145,Ethics and NLP,model bias/fairness evaluation,model analysis & interpretability, +2147,Speech and Multimodality,automatic speech recognition,approaches for low-resource settings, +2151,Machine Learning for NLP,few-shot learning,approaches for low-resource settings, +2152,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",reasoning|paraphrase generation|text simplification,nlp engineering experiment|publicly available software and pre-trained models|data resources|data analysis, +2153,Ethics and NLP,model bias/unfairness mitigation,nlp engineering experiment,german +2154,Machine Learning for NLP,few-shot learning,nlp engineering experiment, +2158,Dialogue and Interactive Systems,multi-modal dialogue systems,data resources|data analysis, +2160,Speech and Multimodality,spoken language translation,nlp engineering experiment, +2164,Summarization,abstractive summarisation,model analysis & interpretability, +2165,Generation,human evaluation|automatic evaluation|analysis|model architectures,nlp engineering experiment,german +2166,Multilingualism and Cross-Lingual NLP,mutlilingual representations,nlp engineering experiment|data resources, +2173,"Syntax: Tagging, Chunking, and Parsing","parsing algorighms (symbolic, theoritical results)",theory, +2176,Linguistic Diversity,less-resourced languages|resources for less-resourced languages,approaches for low-resource settings|data resources,albanian|amharic|armenian|azerbaijani|bangla|bosnian|burmese|dari persian|french|georgian|greek|haitian creole|hausa|indonesian|khmer|kinyarwanda|korean|kurdish|lao|macedonian|mandarin chinese|northern ndebele|pashto|persian farsi|portuguese|russian|serbian|shona|somali|spanish|swahili|thai|tibetan|tigrinya|turkish|ukrainian|urdu|uzbek|vietnamese +2178,NLP Applications,legal nlp,nlp engineering experiment|model analysis & interpretability, +2181,Generation,interactive and collaborative generation,nlp engineering experiment, +2186,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",linguistic theories,model analysis & interpretability|data analysis|theory, +2187,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment, +2192,Generation,human evaluation,data analysis, +2193,Dialogue and Interactive Systems,dialogue state tracking,"approaches for low-compute settings, efficiency", +2197,Large Language Models,security and privacy,nlp engineering experiment, +2200,NLP Applications,mathematical nlp,model analysis & interpretability, +2201,Summarization,query-focused summarization|multi-document summarization,approaches for low-resource settings|publicly available software and pre-trained models, +2204,Large Language Models,prompting|interpretability/analysis,nlp engineering experiment, +2205,Dialogue and Interactive Systems,conversational modeling,nlp engineering experiment, +2206,NLP Applications,financial/business nlp,nlp engineering experiment,japanese +2208,Large Language Models,prompting|applications,nlp engineering experiment|model analysis & interpretability, +2209,Generation,text-to-text generation,nlp engineering experiment|publicly available software and pre-trained models, +2216,NLP Applications,knowledge graphs,nlp engineering experiment|publicly available software and pre-trained models|data resources, +2221,Resources and Evaluation,nlp datasets,data resources, +2222,Machine Translation,automatic evaluation|few-shot/zero-shot mt|human evaluation|pre-training for mt,nlp engineering experiment|reproduction study, +2223,Multilingualism and Cross-Lingual NLP,multilingual benchmarks|multilingual evaluation,model analysis & interpretability|data resources,arabic|korean +2224,Interpretability and Analysis of Models for NLP,data shortcuts/artifacts,model analysis & interpretability, +2226,Computational Social Science and Cultural Analytics,hate-speech detection,nlp engineering experiment|publicly available software and pre-trained models|data resources, +2229,Resources and Evaluation,nlp datasets|evaluation methodologies|evaluation,model analysis & interpretability|data analysis, +2232,Summarization,abstractive summarisation|factuality,nlp engineering experiment|model analysis & interpretability, +2238,Ethics and NLP,model bias/fairness evaluation|model bias/unfairness mitigation|ethical considerations in nlp applications|transparency,model analysis & interpretability, +2239,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",style analysis,data analysis,hebrew|biblical hebrew|ancient hebrew +2240,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",semantic textual similarity,nlp engineering experiment|data analysis|position papers, +2243,Machine Learning for NLP,model compression methods,"nlp engineering experiment|approaches for low-compute settings, efficiency", +2244,Computational Social Science and Cultural Analytics,stance detection|nlp tools for social analysis,nlp engineering experiment|data resources|data analysis, +2247,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",stance detection,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models|data analysis, +2251,Dialogue and Interactive Systems,task-oriented,data resources, +2258,Machine Translation,multilingual mt,approaches for low-resource settings|data resources, +2261,Semantics: Lexical,lexical relationships,nlp engineering experiment, +2262,Large Language Models,prompting,"approaches for low-compute settings, efficiency", +2268,Machine Translation,parallel decoding/non-autoregressive mt,nlp engineering experiment, +2269,Information Retrieval and Text Mining,passage retrieval|dense retrieval|document representation,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models, +2270,Ethics and NLP,model bias/fairness evaluation,nlp engineering experiment|approaches for low-resource settings, +2272,Machine Translation,few-shot/zero-shot mt,approaches for low-resource settings, +2273,NLP Applications,"educational applications, gec, essay scoring",publicly available software and pre-trained models, +2274,Generation,text-to-text generation,publicly available software and pre-trained models|data resources|data analysis, +2283,Ethics and NLP,model bias/fairness evaluation,data resources, +2284,Resources and Evaluation,benchmarking|language resources|multilingual corpora|nlp datasets|automatic evaluation of datasets,data resources|surveys,russian|italian|urdu|japanese|spanish|danish|brazilian portuguese|french|german|slovene|basque +2288,Multilingualism and Cross-Lingual NLP,cross-lingual transfer,approaches for low-resource settings, +2289,Machine Learning for NLP,parameter-efficient finetuning,nlp engineering experiment, +2291,Multilingualism and Cross-Lingual NLP,code-switching|multilingualism|cross-lingual transfer|mutlilingual representations|multilingual evaluation,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings|publicly available software and pre-trained models,spanish-english codemixing|hindi-english codemixing|spanish|french|german|hindi|thai +2296,Machine Learning for NLP,generalization,nlp engineering experiment, +2297,Generation,inference methods,nlp engineering experiment, +2298,Ethics and NLP,model bias/fairness evaluation,model analysis & interpretability|data resources, +2305,Ethics and NLP,model bias/unfairness mitigation,nlp engineering experiment, +2307,Question Answering,semantic parsing|generalization,nlp engineering experiment, +2311,Machine Learning for NLP,human-in-the-loop / active learning,nlp engineering experiment, +2317,Resources and Evaluation,nlp datasets,data resources, +2318,Dialogue and Interactive Systems,task-oriented,nlp engineering experiment, +2320,Interpretability and Analysis of Models for NLP,data shortcuts/artifacts|hardness of samples|robustness,model analysis & interpretability, +2321,Summarization,evaluation,nlp engineering experiment, +2323,Resources and Evaluation,corpus creation,data resources,czech|latin|german +2324,Generation,text-to-text generation|model architectures|retrieval-augmented generation,nlp engineering experiment|publicly available software and pre-trained models, +2325,Computational Social Science and Cultural Analytics,nlp tools for social analysis,data resources, +2330,NLP Applications,code generation and understanding,nlp engineering experiment|data resources, +2331,Multilingualism and Cross-Lingual NLP,linguistic variation|dialects and language varieties,nlp engineering experiment|publicly available software and pre-trained models|data resources, +2333,Linguistic Diversity,less-resourced languages|indigenous languages|resources for less-resourced languages,"approaches for low-compute settings, efficiency|approaches for low-resource settings",assamese +2336,Large Language Models,fine-tuning,approaches for low-resource settings, +2339,Multilingualism and Cross-Lingual NLP,mutlilingual representations,nlp engineering experiment, +2343,Computational Social Science and Cultural Analytics,hate-speech detection|sociolinguistics,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models|data resources|data analysis, +2349,Machine Learning for NLP,graph-based methods|self-supervised learning,nlp engineering experiment, +2350,Large Language Models,fine-tuning,nlp engineering experiment|publicly available software and pre-trained models, +2353,"Language Grounding to Vision, Robotics, and Beyond",cross-modal pretraining,nlp engineering experiment|approaches for low-resource settings, +2354,NLP Applications,"healthcare applications, clincial nlp",data resources, +2356,Interpretability and Analysis of Models for NLP,probing|robustness,model analysis & interpretability, +2357,Dialogue and Interactive Systems,conversational modeling,"nlp engineering experiment|approaches for low-compute settings, efficiency", +2358,Large Language Models,interpretability/analysis,model analysis & interpretability|data analysis|surveys, +2364,Question Answering,semantic parsing,nlp engineering experiment, +2367,Semantics: Lexical,compositionality,approaches for low-resource settings|theory, +2373,Interpretability and Analysis of Models for NLP,free-text/natural language explanations,nlp engineering experiment|model analysis & interpretability, +2376,Generation,automatic evaluation,"nlp engineering experiment|approaches for low-compute settings, efficiency|publicly available software and pre-trained models|data analysis", +2379,NLP Applications,"healthcare applications, clincial nlp",publicly available software and pre-trained models|data resources, +2381,Discourse and Pragmatics,coreference resolution,nlp engineering experiment|publicly available software and pre-trained models|data resources, +2383,Discourse and Pragmatics,coreference resolution,"nlp engineering experiment|approaches for low-compute settings, efficiency", +2388,Large Language Models,prompting,reproduction study|position papers, +2391,"Language Grounding to Vision, Robotics, and Beyond",cross-modal pretraining,"approaches for low-compute settings, efficiency", +2392,Resources and Evaluation,corpus creation|multilingual corpora|nlp datasets,model analysis & interpretability|data resources,arabic|hindi +2393,Speech and Multimodality,automatic speech recognition|spoken language translation,nlp engineering experiment|approaches for low-resource settings, +2396,Interpretability and Analysis of Models for NLP,calibration/uncertainty,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models, +2401,NLP Applications,hate speech detection,nlp engineering experiment|model analysis & interpretability, +2402,Interpretability and Analysis of Models for NLP,calibration/uncertainty,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models, +2404,Machine Learning for NLP,model compression methods,"approaches for low-compute settings, efficiency", +2405,Interpretability and Analysis of Models for NLP,robustness,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models, +2406,Theme Track: Reality Check,evaluation,nlp engineering experiment|model analysis & interpretability|theory, +2407,Resources and Evaluation,corpus creation,nlp engineering experiment|data resources|data analysis, +2408,Summarization,abstractive summarisation|query-focused summarization|long-form summarization|few-shot summarisation,publicly available software and pre-trained models|data resources|data analysis, +2410,Interpretability and Analysis of Models for NLP,topic modeling,nlp engineering experiment, +2411,Interpretability and Analysis of Models for NLP,robustness,model analysis & interpretability, +2412,Interpretability and Analysis of Models for NLP,free-text/natural language explanations,model analysis & interpretability, +2416,Multilingualism and Cross-Lingual NLP,cross-lingual transfer,approaches for low-resource settings, +2418,Machine Learning for NLP,model compression methods,nlp engineering experiment, +2419,Question Answering,reasoning,nlp engineering experiment|data resources, +2421,Ethics and NLP,model bias/fairness evaluation,model analysis & interpretability, +2422,Information Extraction,named entity recognition and relation extraction,"nlp engineering experiment|approaches for low-compute settings, efficiency|publicly available software and pre-trained models", +2423,"Syntax: Tagging, Chunking, and Parsing",semantic parsing,nlp engineering experiment, +2426,"Syntax: Tagging, Chunking, and Parsing",semantic parsing,nlp engineering experiment, +2428,Dialogue and Interactive Systems,task-oriented,approaches for low-resource settings, +2429,Theme Track: Reality Check,negative results,nlp engineering experiment|model analysis & interpretability|data resources, +2433,Summarization,abstractive summarisation,nlp engineering experiment, +2435,Discourse and Pragmatics,coherence|discourse relations|discourse parsing,nlp engineering experiment|data resources|theory, +2437,"Language Grounding to Vision, Robotics, and Beyond",vision question answering,nlp engineering experiment, +2439,Machine Translation,multilingual mt|multimodality,nlp engineering experiment|approaches for low-resource settings,german +2441,Interpretability and Analysis of Models for NLP,counterfactual/contrastive explanations|explanation faithfulness|feature attribution,model analysis & interpretability, +2448,Machine Learning for NLP,model compression methods,"approaches for low-compute settings, efficiency", +2449,Large Language Models,fine-tuning,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings|theory, +2456,Machine Learning for NLP,data augmentation,"approaches for low-compute settings, efficiency", +2460,NLP Applications,multimodal applications,nlp engineering experiment, +2461,Theme Track: Reality Check,(non-)generalizability,nlp engineering experiment|model analysis & interpretability, +2462,Computational Social Science and Cultural Analytics,nlp tools for social analysis|quantiative analyses of news and/or social media,model analysis & interpretability|data resources|data analysis, +2465,Ethics and NLP,participatory/community-based nlp,nlp engineering experiment|model analysis & interpretability|data resources, +2466,Dialogue and Interactive Systems,conversational modeling,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models|data resources, +2469,Dialogue and Interactive Systems,commonsense reasoning,nlp engineering experiment|publicly available software and pre-trained models|data resources, +2476,Machine Learning for NLP,representation learning,nlp engineering experiment|data resources, +2480,Information Extraction,named entity recognition and relation extraction|event extraction|document-level extraction,nlp engineering experiment|model analysis & interpretability, +2484,"Language Grounding to Vision, Robotics, and Beyond",cross-modal information extraction,nlp engineering experiment, +2489,Information Extraction,entity linking/disambiguation,nlp engineering experiment, +2492,"Language Grounding to Vision, Robotics, and Beyond",image text matching|vision question answering,nlp engineering experiment|model analysis & interpretability|reproduction study, +2498,Information Extraction,document-level extraction,nlp engineering experiment|approaches for low-resource settings, +2500,Speech and Multimodality,multimodality,nlp engineering experiment, +2506,Theme Track: Reality Check,ai hype & expectations,position papers|surveys, +2507,NLP Applications,"educational applications, gec, essay scoring",model analysis & interpretability,chinese +2511,Interpretability and Analysis of Models for NLP,probing,model analysis & interpretability, +2512,"Language Grounding to Vision, Robotics, and Beyond",vision question answering|cross-modal application,nlp engineering experiment|model analysis & interpretability|reproduction study, +2515,Speech and Multimodality,multimodality,publicly available software and pre-trained models, +2519,Dialogue and Interactive Systems,applications,nlp engineering experiment, +2521,NLP Applications,"fact checking, rumour/misinformation detection",nlp engineering experiment, +2522,Machine Learning for NLP,structured prediction,"approaches for low-compute settings, efficiency", +2525,"Phonology, Morphology, and Word Segmentation",morphological analysis,"nlp engineering experiment|approaches for low-compute settings, efficiency",japanese +2526,Machine Learning for NLP,generative models|optimization methods,nlp engineering experiment, +2529,"Language Grounding to Vision, Robotics, and Beyond",cross-modal pretraining|cross-modal application,nlp engineering experiment|publicly available software and pre-trained models, +2530,NLP Applications,"healthcare applications, clincial nlp",nlp engineering experiment|publicly available software and pre-trained models|data resources, +2531,Large Language Models,pre-training,"approaches for low-compute settings, efficiency", +2533,"Language Grounding to Vision, Robotics, and Beyond",vision language navigation,model analysis & interpretability, +2534,Ethics and NLP,model bias/unfairness mitigation,nlp engineering experiment, +2536,Large Language Models,pre-training,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency|publicly available software and pre-trained models", +2538,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",argument mining|applications,nlp engineering experiment|model analysis & interpretability|surveys, +2540,Theme Track: Reality Check,lessons from deployment|(non-)generalizability|negative results,nlp engineering experiment|model analysis & interpretability, +2547,Generation,inference methods,nlp engineering experiment,chinese +2550,Question Answering,,nlp engineering experiment|approaches for low-resource settings, +2560,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training,model analysis & interpretability|reproduction study, +2562,Interpretability and Analysis of Models for NLP,free-text/natural language explanations,model analysis & interpretability,chinese +2565,Machine Learning for NLP,model compression methods,nlp engineering experiment, +2576,Dialogue and Interactive Systems,task-oriented|multilingual / low resource,approaches for low-resource settings, +2577,Resources and Evaluation,nlp datasets,nlp engineering experiment|data resources|data analysis, +2579,Machine Learning for NLP,few-shot learning,approaches for low-resource settings, +2581,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",stance detection,nlp engineering experiment|model analysis & interpretability|data analysis|reproduction study|surveys, +2582,Summarization,evaluation|factuality,nlp engineering experiment|model analysis & interpretability, +2587,Dialogue and Interactive Systems,applications|conversational modeling,nlp engineering experiment, +2589,Theme Track: Reality Check,evaluation,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings, +2592,Machine Learning for NLP,word embeddings,nlp engineering experiment, +2596,Information Extraction,event extraction|document-level extraction,nlp engineering experiment, +2599,Summarization,extractive summarisation|architectures,nlp engineering experiment, +2601,NLP Applications,legal nlp,nlp engineering experiment, +2602,NLP Applications,"healthcare applications, clincial nlp",nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models|data resources|data analysis, +2606,Generation,model architectures,nlp engineering experiment|publicly available software and pre-trained models, +2607,Machine Learning for NLP,data augmentation,nlp engineering experiment|model analysis & interpretability,chinese +2609,Machine Learning for NLP,model compression methods,"approaches for low-compute settings, efficiency|approaches for low-resource settings|publicly available software and pre-trained models|theory", +2616,Resources and Evaluation,evaluation methodologies|evaluation,nlp engineering experiment|data resources, +2618,Summarization,query-focused summarization,nlp engineering experiment, +2621,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models|data analysis, +2622,Interpretability and Analysis of Models for NLP,explanation faithfulness|free-text/natural language explanations,model analysis & interpretability, +2625,Machine Learning for NLP,model compression methods,"approaches for low-compute settings, efficiency", +2627,Computational Social Science and Cultural Analytics,stance detection,nlp engineering experiment|data analysis, +2629,Multilingualism and Cross-Lingual NLP,multilingualism,data analysis|theory,malay|chinese +2630,Question Answering,multihop qa|open-domain qa,publicly available software and pre-trained models, +2633,Information Extraction,named entity recognition and relation extraction,"nlp engineering experiment|approaches for low-compute settings, efficiency|approaches for low-resource settings", +2634,Multilingualism and Cross-Lingual NLP,multilingualism|cross-lingual transfer|multilingual evaluation,nlp engineering experiment, +2635,"Phonology, Morphology, and Word 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Mining",applications,approaches for low-resource settings, +2663,Machine Translation,automatic evaluation,nlp engineering experiment, +2664,NLP Applications,legal nlp|security/privacy,nlp engineering experiment, +2666,Speech and Multimodality,speech technologies|multimodality,"model analysis & interpretability|approaches for low-compute settings, efficiency|reproduction study", +2678,Resources and Evaluation,nlp datasets,nlp engineering experiment|model analysis & interpretability|data resources|data analysis, +2681,Machine Learning for NLP,generalization,nlp engineering experiment, +2684,Question Answering,open-domain qa,nlp engineering experiment, +2686,NLP Applications,multimodal applications,nlp engineering experiment|model analysis & interpretability, +2689,Dialogue and Interactive Systems,bias/toxicity,nlp engineering experiment, +2691,Speech and Multimodality,automatic speech recognition,publicly available software and pre-trained models, +2699,Large Language Models,interpretability/analysis,model analysis & interpretability, +2700,Interpretability and Analysis of Models for NLP,data shortcuts/artifacts,model analysis & interpretability, +2701,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment, +2703,Ethics and NLP,model bias/fairness evaluation|model bias/unfairness mitigation|human factors in nlp,nlp engineering experiment|position papers, +2706,Large Language Models,pre-training|prompting|retrieval-augmented models,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models, +2712,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",applications,data resources, +2713,Speech and Multimodality,multimodality,nlp engineering experiment, +2714,Summarization,conversational summarization,data resources,chinese +2715,"Language Grounding to Vision, Robotics, and Beyond",cross-modal information extraction,nlp engineering experiment, +2717,Resources and Evaluation,corpus creation|nlp datasets|evaluation,model analysis & interpretability|data resources, +2718,"Language Grounding to Vision, Robotics, and Beyond",cross-modal content generation,nlp engineering experiment, +2722,NLP Applications,legal nlp,data resources|data analysis, +2728,Machine Translation,speech translation,nlp engineering experiment, +2730,Information Extraction,named entity recognition and relation extraction|zero/few-shot extraction,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models, +2734,Machine Learning for NLP,parameter-efficient finetuning,nlp engineering experiment|reproduction study, +2736,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",stance detection,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings, +2740,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",reasoning,"approaches for low-compute settings, efficiency|data resources", +2746,Multilingualism and Cross-Lingual NLP,code-switching,publicly available software and pre-trained models|data resources,spanish +2749,Machine Learning for NLP,model compression methods|parameter-efficient finetuning,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency", +2755,Machine Learning for NLP,transfer learning / domain adaptation,model analysis & interpretability, +2766,Dialogue and Interactive Systems,grounded dialog,nlp engineering experiment|approaches for low-resource settings, +2767,Dialogue and Interactive Systems,dialogue state tracking,nlp engineering experiment, +2768,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",stance detection,nlp engineering experiment|data resources,chinese +2769,Dialogue and Interactive Systems,conversational modeling,nlp engineering experiment, +2772,Theme Track: Reality Check,methodology,publicly available software and pre-trained models, +2774,Summarization,query-focused summarization,data resources, +2776,Speech and Multimodality,multimodality,nlp engineering experiment, +2785,Machine Learning for NLP,parameter-efficient finetuning,"approaches for low-compute settings, efficiency", +2786,Machine Learning for NLP,parameter-efficient finetuning|meta learning,nlp engineering experiment, +2788,Ethics and NLP,model bias/unfairness mitigation,nlp engineering experiment, +2796,Dialogue and Interactive Systems,evaluation and metrics,nlp engineering experiment, +2797,Dialogue and Interactive Systems,spoken dialogue systems|multi-modal dialogue systems|conversational modeling,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency", +2800,Resources and Evaluation,nlp datasets,nlp engineering experiment|data resources|data analysis, +2812,Machine Learning for NLP,model compression methods,"approaches for low-compute settings, efficiency", +2823,Question Answering,reasoning,nlp engineering experiment, +2824,Information Extraction,event extraction,nlp engineering experiment, +2826,Information Extraction,named entity recognition and relation extraction|event extraction,nlp engineering experiment, +2827,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training,nlp engineering experiment, +2830,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",applications,nlp engineering experiment, +2832,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment|approaches for low-resource settings, +2835,Machine Translation,speech translation,nlp engineering experiment, +2841,Information Extraction,named entity recognition and relation extraction|zero/few-shot extraction,nlp engineering experiment|approaches for low-resource settings, +2842,Dialogue and Interactive Systems,dialogue state tracking,nlp engineering experiment, +2843,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training|robustness,model analysis & interpretability, +2848,Machine Translation,efficient mt training,nlp engineering experiment, +2852,Dialogue and Interactive Systems,,nlp engineering experiment, +2855,Machine Translation,multimodality,publicly available software and pre-trained models|data resources, +2859,Machine Translation,few-shot/zero-shot mt,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings, +2860,Dialogue and Interactive Systems,multi-modal dialogue systems,data resources, +2862,Information Retrieval and Text Mining,re-ranking,nlp engineering experiment, +2863,Question Answering,logical reasoning|knowledge base qa|generalization,nlp engineering experiment, +2865,NLP Applications,"healthcare applications, clincial nlp",nlp engineering experiment, +2866,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training,nlp engineering experiment, +2868,NLP Applications,financial/business nlp|knowledge graphs,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency|publicly available software and pre-trained models|data resources", +2870,Summarization,abstractive summarisation,nlp engineering experiment, +2873,Machine Translation,interactive mt,nlp engineering experiment, +2874,NLP Applications,"healthcare applications, clincial nlp",model analysis & interpretability, +2876,Theme Track: Reality Check,evaluation|ai hype & expectations,data analysis,german|japanese|ukrainian +2877,Large Language Models,fine-tuning,nlp engineering experiment, +2879,Dialogue and Interactive Systems,task-oriented,nlp engineering experiment, +2884,NLP Applications,knowledge graphs,nlp engineering experiment, +2885,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",applications,data resources, +2887,Machine Learning for NLP,multi-task learning|data augmentation|model compression methods,"approaches for low-compute settings, efficiency|approaches for low-resource settings", +2891,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",cognitive modeling,nlp engineering experiment, +2893,Machine Translation,speech translation,nlp engineering experiment|data resources,japanese +2901,NLP Applications,knowledge graphs,nlp engineering experiment|theory, +2907,"Language Grounding to Vision, Robotics, and Beyond",cross-modal application,nlp engineering experiment|model analysis & interpretability, +2909,Large Language Models,fine-tuning,"approaches for low-compute settings, efficiency", +2910,Theme Track: Reality Check,right for the wrong reasons,surveys, +2912,Dialogue and Interactive Systems,spoken dialogue systems,nlp engineering experiment, +2915,NLP Applications,knowledge graphs,nlp engineering experiment, +2920,Machine Learning for NLP,self-supervised learning,nlp engineering experiment, +2923,Information Retrieval and Text Mining,passage retrieval,nlp engineering experiment, +2928,Dialogue and Interactive Systems,applications,nlp engineering experiment,chinese +2930,Machine Learning for NLP,data augmentation,nlp engineering experiment, +2934,Ethics and NLP,model bias/unfairness mitigation|ethical considerations in nlp applications,nlp engineering experiment, +2936,Information Extraction,named entity recognition and relation extraction|zero/few-shot extraction,nlp engineering experiment|approaches for low-resource settings, +2939,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",argument mining,nlp engineering experiment, +2942,Discourse and Pragmatics,anaphora resolution,model analysis & interpretability, +2943,"Phonology, Morphology, and Word Segmentation",phonology,nlp engineering experiment,romance languages|chinese +2944,Large Language Models,pre-training,"approaches for low-compute settings, efficiency", +2946,Resources and Evaluation,corpus creation|automatic creation and evaluation of language resources|nlp datasets,model analysis & interpretability|data resources, +2952,Multilingualism and Cross-Lingual NLP,cross-lingual transfer,publicly available software and pre-trained models, +2953,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",reasoning,nlp engineering experiment|approaches for low-resource settings|data resources, +2957,Large Language Models,prompting|applications,nlp engineering experiment|approaches for low-resource settings, +2961,NLP Applications,"healthcare applications, clincial nlp",approaches for low-resource settings, +2962,Large Language Models,prompting|fine-tuning,nlp engineering experiment|approaches for low-resource settings|data resources, +2966,Information Retrieval and Text Mining,dense retrieval,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings, +2970,Question Answering,semantic parsing,approaches for low-resource settings, +2971,Speech and Multimodality,speech and vision|multimodality,nlp engineering experiment, +2984,Question Answering,reading comprehension,nlp engineering experiment, +2985,Machine Translation,automatic evaluation,model analysis & interpretability, +2987,Summarization,multimodal summarization,nlp engineering experiment, +2991,Resources and Evaluation,automatic creation and evaluation of language resources,approaches for low-resource settings, +2992,Summarization,multimodal summarization,data resources, +2993,"Syntax: Tagging, Chunking, and Parsing",multi-task approaches (large definition),nlp engineering experiment,chinese +3000,Interpretability and Analysis of Models for NLP,data influence|data shortcuts/artifacts|hardness of samples,"approaches for low-compute settings, efficiency|data resources|data analysis", +3004,Machine Learning for NLP,reinforcement learning,nlp engineering experiment, +3005,Speech and Multimodality,speech and vision,nlp engineering experiment,mandarin +3011,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",rhetoric and framing,nlp engineering experiment, +3014,Question Answering,generalization,data resources, +3017,Machine Translation,speech translation,nlp engineering experiment|approaches for low-resource settings, +3022,Generation,text-to-text generation,nlp engineering experiment, +3024,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment|approaches for low-resource settings, +3032,Information Extraction,named entity recognition and relation extraction|event extraction,nlp engineering experiment|theory, +3035,Interpretability and Analysis of Models for NLP,hierarchical & concept explanations,model analysis & interpretability, +3040,"Language Grounding to Vision, Robotics, and Beyond",cross-modal application,nlp engineering experiment, +3042,Large Language Models,fine-tuning,approaches for low-resource settings, +3043,Machine Translation,multimodality,model analysis & interpretability, +3047,Multilingualism and Cross-Lingual NLP,cross-lingual transfer|mutlilingual representations,model analysis & interpretability, +3052,Large Language Models,retrieval-augmented models|interpretability/analysis,model analysis & interpretability|reproduction study, +3057,Dialogue and Interactive Systems,applications,nlp engineering experiment|publicly available software and pre-trained models|data analysis, +3058,Dialogue and Interactive Systems,knowledge augmented|grounded dialog,nlp engineering experiment, +3059,Resources and Evaluation,evaluation methodologies|evaluation|statistical testing for evaluation,theory, +3061,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment, +3062,Interpretability and Analysis of Models for NLP,explanation faithfulness|feature attribution,model analysis & interpretability, +3065,"Language Grounding to Vision, Robotics, and Beyond",image text matching,nlp engineering experiment|model analysis & interpretability, +3066,Information Retrieval and Text Mining,re-ranking,nlp engineering experiment|approaches for low-resource settings, +3069,Machine Translation,speech translation,approaches for low-resource settings, +3071,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",computational psycholinguistics,model analysis & interpretability, +3072,Question Answering,generalization,nlp engineering experiment, +3074,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",linguistic theories,model analysis & interpretability, +3081,Machine Learning for NLP,representation learning,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models, +3082,Generation,analysis,nlp engineering experiment, +3085,Resources and Evaluation,reproducibility,reproduction study, +3087,Generation,few-shot generation|text-to-text generation|interactive and collaborative generation,nlp engineering experiment|data resources, +3091,"Syntax: Tagging, Chunking, and Parsing",dependency parsing,nlp engineering experiment|model analysis & interpretability, +3093,Generation,model architectures,theory, +3101,Machine Translation,automatic evaluation|biases,model analysis & interpretability, +3103,Dialogue and Interactive Systems,bias/toxicity,nlp engineering experiment, +3107,Resources and Evaluation,nlp datasets|evaluation|metrics,data resources|data analysis, +3109,"Language Grounding to Vision, Robotics, and Beyond",cross-modal pretraining,nlp engineering experiment, +3114,NLP Applications,knowledge graphs,"nlp engineering experiment|approaches for low-compute settings, efficiency|data analysis", +3115,Resources and Evaluation,automatic evaluation of datasets,nlp engineering experiment, +3116,NLP Applications,legal nlp,publicly available software and pre-trained models|data resources|data analysis, +3117,Generation,text-to-text generation,nlp engineering experiment, +3121,Multilingualism and Cross-Lingual NLP,mutlilingual representations,nlp engineering experiment, +3124,Speech and Multimodality,spoken language understanding|multimodality,nlp engineering experiment, +3125,Machine Learning for NLP,model compression methods,"nlp engineering experiment|approaches for low-compute settings, efficiency", +3126,Dialogue and Interactive Systems,multilingual / low resource,data resources, +3129,Machine Learning for NLP,graph-based methods|representation learning|graphical models,position papers, +3135,Machine Translation,efficient mt training,model analysis & interpretability, +3138,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +3145,Resources and Evaluation,nlp datasets,data resources, +3147,Information Extraction,named entity recognition and relation extraction|event extraction,nlp engineering experiment, +3150,Ethics and NLP,model bias/unfairness mitigation,nlp engineering experiment, +3152,Machine Translation,few-shot/zero-shot mt|modelling,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings|publicly available software and pre-trained models,german|polish|french +3153,Information Retrieval and Text Mining,passage retrieval|document representation,nlp engineering experiment|model analysis & interpretability, +3155,Ethics and NLP,human factors in nlp,nlp engineering experiment, +3160,Machine Translation,modelling,nlp engineering experiment, +3161,Theme Track: Reality Check,(non-)reproducibility|evaluation|methodology,data resources|data analysis|position papers|surveys, +3165,Dialogue and Interactive Systems,task-oriented|multilingual / low resource,nlp engineering experiment|data resources, +3167,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment|data resources|data analysis, +3168,Semantics: Lexical,polysemy,publicly available software and pre-trained models,chinese +3174,Machine Translation,speech translation,theory, +3175,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",linguistic theories|computational psycholinguistics,model analysis & interpretability,spanish|galician +3178,Generation,text-to-text generation,nlp engineering experiment,chinese +3179,Resources and Evaluation,corpus creation|language resources|nlp datasets|evaluation,model analysis & interpretability|data resources|data analysis, +3180,Theme Track: Reality Check,lessons from deployment|evaluation|forgotten lessons,surveys, +3183,Large Language Models,retrieval-augmented models,model analysis & interpretability|approaches for low-resource settings, +3184,Resources and Evaluation,language resources,data resources,french|german|italian|spanish +3187,Question Answering,open-domain qa,model analysis & interpretability|reproduction study, +3188,Theme Track: Reality Check,lessons from deployment|evaluation,nlp engineering experiment|model analysis & interpretability|reproduction study, +3190,Dialogue and Interactive Systems,evaluation and metrics|conversational modeling,nlp engineering experiment|publicly available software and pre-trained models, +3191,Information Extraction,event extraction|document-level extraction,nlp engineering experiment, +3193,Machine Learning for NLP,graph-based methods,nlp engineering experiment, +3197,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",reasoning,nlp engineering experiment|approaches for low-resource settings, +3198,Generation,data-to-text generation,nlp engineering experiment, +3203,Theme Track: Reality Check,(non-)generalizability|evaluation,model analysis & interpretability|data resources,german|italian|french|farsi|danish +3205,Resources and Evaluation,corpus creation|benchmarking|language resources|multilingual corpora|datasets for low resource languages,approaches for low-resource settings|publicly available software and pre-trained models|data resources,assamese|bodo|bengali|dogri|konkani|gujarati|hindi|khasi|kannada|kashmiri|maithili|malayalam|manipuri|marathi|nepali|odia|punjabi|sanskrit|santhali|sindhi|tamil|telugu|urdu +3209,Resources and Evaluation,evaluation methodologies,nlp engineering experiment, +3216,Question Answering,few-shot qa,nlp engineering experiment, +3221,NLP Applications,"educational applications, gec, essay scoring|mathematical nlp",nlp engineering experiment, +3225,Theme Track: Reality Check,evaluation,publicly available software and pre-trained models|position papers, +3227,Theme Track: Reality Check,(non-)generalizability|evaluation,model analysis & interpretability|approaches for low-resource settings|reproduction study, +3228,Generation,model architectures,nlp engineering experiment, +3229,Large Language Models,prompting|fine-tuning,"nlp engineering experiment|approaches for low-compute settings, efficiency|approaches for low-resource settings", +3231,"Syntax: Tagging, Chunking, and Parsing",semantic parsing,nlp engineering experiment, +3234,Summarization,abstractive summarisation|multi-document summarization,nlp engineering experiment|approaches for low-resource settings, +3241,Machine Translation,domain adaptation,nlp engineering experiment, +3242,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment, +3243,Information Extraction,entity linking/disambiguation,nlp engineering experiment, +3245,NLP Applications,,data resources, +3246,Speech and Multimodality,speech technologies,nlp engineering experiment, +3250,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +3259,Large Language Models,prompting,nlp engineering experiment, +3267,Machine Learning for NLP,model compression methods,"nlp engineering experiment|approaches for low-compute settings, efficiency", +3272,NLP Applications,security/privacy,nlp engineering experiment, +3273,NLP Applications,,nlp engineering experiment,spanish|french|german|italian|dutch|portuguese|romanian +3275,Dialogue and Interactive Systems,conversational modeling,nlp engineering experiment, +3281,Machine Learning for NLP,generalization|parameter-efficient finetuning,nlp engineering experiment|approaches for low-resource settings, +3287,Machine Learning for NLP,human-in-the-loop / active learning,nlp engineering experiment|approaches for low-resource settings, +3288,Interpretability and Analysis of Models for NLP,robustness,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency|theory", +3291,Speech and Multimodality,automatic speech recognition,nlp engineering experiment|approaches for low-resource settings,gronings|west-frisian|besemah|nasal +3294,Large Language Models,robustness|fine-tuning,nlp engineering experiment, +3301,"Language Grounding to Vision, Robotics, and Beyond",cross-modal pretraining,nlp engineering experiment, +3302,Question Answering,commonsense qa,data resources, +3304,Speech and Multimodality,multimodality,model analysis & interpretability|data resources, +3308,Discourse and Pragmatics,coherence,nlp engineering experiment|data resources|data analysis, +3309,Large Language Models,prompting|scaling|ethics|security and privacy|applications,"nlp engineering experiment|approaches for low-compute settings, efficiency", +3313,Summarization,abstractive summarisation,publicly available software and pre-trained models,german +3320,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models, +3322,Ethics and NLP,transparency|policy and governance|reflections and critiques,position papers, +3324,Machine Learning for NLP,parameter-efficient finetuning,"approaches for low-compute settings, efficiency", +3325,Generation,text-to-text generation,nlp engineering experiment, +3326,NLP Applications,"fact checking, rumour/misinformation detection",data resources, +3327,Computational Social Science and Cultural Analytics,stance detection,nlp engineering experiment, +3329,Multilingualism and Cross-Lingual NLP,multilingual pre-training,publicly available software and pre-trained models, +3332,Interpretability and Analysis of Models for NLP,free-text/natural language explanations,nlp engineering experiment|model analysis & interpretability, +3333,NLP Applications,hate speech detection,nlp engineering experiment|data resources, +3334,Computational Social Science and Cultural Analytics,hate-speech detection,data resources, +3340,Machine Learning for NLP,data augmentation,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings|publicly available software and pre-trained models,basque +3342,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",applications,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models, +3343,"Syntax: Tagging, Chunking, and Parsing","constituency parsing|parsing algorighms (symbolic, theoritical results)",theory, +3344,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +3353,Question Answering,table qa,nlp engineering experiment, +3356,Large Language Models,scaling,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency|approaches for low-resource settings|publicly available software and pre-trained models",basque|spanish|swahili|finnish +3357,Resources and Evaluation,benchmarking|multilingual corpora|nlp datasets|datasets for low resource languages,approaches for low-resource settings|publicly available software and pre-trained models|data resources,assamese|bodo|bengali|dogri|konkani|gujarati|hindi|khasi|kannada|kashmiri|maithili|malayalam|manipuri|marathi|nepali|odia|punjabi|sanskrit|santhali|sindhi|tamil|telugu|urdu|indian +3361,Computational Social Science and Cultural Analytics,emotion detection and analysis,approaches for low-resource settings, +3367,Summarization,extractive summarisation,nlp engineering experiment, +3371,Resources and Evaluation,corpus creation|language resources|nlp datasets|datasets for low resource languages,data resources,hebrew +3374,Machine Learning for NLP,generalization,nlp engineering experiment, +3377,Generation,analysis,nlp engineering experiment, +3378,Machine Learning for NLP,model compression methods,"approaches for low-compute settings, efficiency", +3380,Information Extraction,named entity recognition and relation extraction,approaches for low-resource settings, +3382,"Phonology, Morphology, and Word Segmentation",morphological inflection|morphological analysis|phonology,model analysis & interpretability|approaches for low-resource settings|theory,arabic +3384,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +3388,NLP Applications,code generation and understanding,data resources, +3389,Resources and Evaluation,evaluation,reproduction study, +3390,Ethics and NLP,model bias/unfairness mitigation,"approaches for low-compute settings, efficiency|surveys", +3391,Speech and Multimodality,speech and vision|multimodality,nlp engineering experiment, +3397,Question Answering,multimodal qa,model analysis & interpretability, +3398,Question Answering,knowledge base qa,nlp engineering experiment, +3400,Interpretability and Analysis of Models for NLP,probing,model analysis & interpretability, +3403,Computational Social Science and Cultural Analytics,nlp tools for social analysis,nlp engineering experiment|model analysis & interpretability|data resources, +3410,Interpretability and Analysis of Models for NLP,probing,model analysis & interpretability, +3412,Speech and Multimodality,multimodality,nlp engineering experiment|approaches for low-resource settings, +3413,"Phonology, Morphology, and Word Segmentation",morphological inflection,approaches for low-resource settings,arabic +3417,Machine Learning for NLP,parameter-efficient finetuning,approaches for low-resource settings, +3418,Question Answering,commonsense qa|reasoning,nlp engineering experiment|model analysis & interpretability, +3420,Computational Social Science and Cultural Analytics,nlp tools for social analysis,nlp engineering experiment|data resources, +3421,"Language Grounding to Vision, Robotics, and Beyond",cross-modal content generation,nlp engineering experiment|publicly available software and pre-trained models, +3422,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",applications,nlp engineering experiment|model analysis & interpretability, +3426,Large Language Models,security and privacy,nlp engineering experiment, +3430,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +3432,Discourse and Pragmatics,dialogue|conversation|communication,nlp engineering experiment, +3434,Semantics: Lexical,sentiment analysis,nlp engineering experiment, +3438,Resources and Evaluation,automatic creation and evaluation of language resources,nlp engineering experiment|approaches for low-resource settings, +3441,Computational Social Science and Cultural Analytics,hate-speech detection,data analysis, +3443,NLP Applications,knowledge graphs,approaches for low-resource settings, +3446,Resources and Evaluation,evaluation,nlp engineering experiment, +3447,Information Extraction,document-level extraction,nlp engineering experiment|approaches for low-resource settings, +3448,Discourse and Pragmatics,coreference resolution,nlp engineering experiment, +3453,Resources and Evaluation,corpus creation|benchmarking,nlp engineering experiment|publicly available software and pre-trained models|data resources|data analysis, +3454,"Syntax: Tagging, Chunking, and Parsing",constituency parsing,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings, +3455,Interpretability and Analysis of Models for NLP,feature attribution,nlp engineering experiment|model analysis & interpretability, +3457,Machine Learning for NLP,representation learning,nlp engineering experiment, +3458,Generation,automatic evaluation,nlp engineering experiment, +3463,Machine Translation,automatic evaluation,nlp engineering experiment, +3467,"Language Grounding to Vision, Robotics, and Beyond",vision language navigation,nlp engineering experiment, +3468,Information Extraction,knowledge base construction,nlp engineering experiment|data resources|data analysis, +3469,"Language Grounding to Vision, Robotics, and Beyond",cross-modal content generation,model analysis & interpretability|data resources, +3472,Theme Track: Reality Check,evaluation,nlp engineering experiment, +3473,Dialogue and Interactive Systems,applications,approaches for low-resource settings, +3475,Large Language Models,security and privacy,"nlp engineering experiment|approaches for low-compute settings, efficiency", +3482,"Syntax: Tagging, Chunking, and Parsing",semantic parsing,approaches for low-resource settings, +3483,Ethics and NLP,ethical considerations in nlp applications,nlp engineering experiment|model analysis & interpretability, +3484,Theme Track: Reality Check,right for the wrong reasons|(non-)generalizability|evaluation|ai hype & expectations,model analysis & interpretability|publicly available software and pre-trained models|data resources, +3486,NLP Applications,"healthcare applications, clincial nlp",nlp engineering experiment|data resources, +3487,NLP Applications,"healthcare applications, clincial nlp",nlp engineering experiment|publicly available software and pre-trained models|data resources,chinese +3490,Resources and Evaluation,benchmarking|nlp datasets,data resources, +3493,Computational Social Science and Cultural Analytics,language/cultural bias analysis,data analysis, +3495,Resources and Evaluation,benchmarking|nlp datasets|evaluation|metrics,data resources|data analysis, +3497,Information Extraction,event extraction|document-level extraction,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models, +3504,"Syntax: Tagging, Chunking, and Parsing",semantic parsing,nlp engineering experiment, +3506,Question Answering,semantic parsing,approaches for low-resource settings, +3512,Interpretability and Analysis of Models for NLP,feature attribution,model analysis & interpretability, +3517,Generation,data-to-text generation,nlp engineering experiment|model analysis & interpretability, +3518,Question Answering,table qa|open-domain qa,nlp engineering experiment|publicly available software and pre-trained models, +3519,"Syntax: Tagging, Chunking, and Parsing",part-of-speech tagging,nlp engineering experiment|position papers, +3523,Semantics: Lexical,interpretability,model analysis & interpretability, +3524,Resources and Evaluation,corpus creation|language resources|nlp datasets,data resources, +3526,Dialogue and Interactive Systems,,nlp engineering experiment,chinese +3530,Dialogue and Interactive Systems,multi-modal dialogue systems,data resources, +3533,Multilingualism and Cross-Lingual NLP,mixed language,data resources,indonesian|malay|singlish +3536,Generation,efficient models,nlp engineering experiment, +3537,Question Answering,table qa,nlp engineering experiment|publicly available software and pre-trained models, +3539,NLP Applications,mathematical nlp,nlp engineering experiment, +3540,Question Answering,logical reasoning|reasoning|math qa,publicly available software and pre-trained models|data analysis, +3541,Information Extraction,knowledge base construction,nlp engineering experiment, +3542,Machine Learning for NLP,graph-based methods|word embeddings,nlp engineering experiment|approaches for low-resource settings, +3546,Resources and Evaluation,nlp datasets,data resources, +3547,NLP Applications,legal nlp,nlp engineering experiment, +3548,Large Language Models,retrieval-augmented models,nlp engineering experiment, +3549,Information Retrieval and Text Mining,passage retrieval|dense retrieval,nlp engineering experiment, +3557,Theme Track: Reality Check,evaluation,position papers|surveys, +3561,Machine Learning for NLP,transfer learning / domain adaptation,nlp engineering experiment, +3562,Question Answering,logical reasoning,nlp engineering experiment, +3563,Interpretability and Analysis of Models for NLP,probing,model analysis & interpretability, +3568,"Syntax: Tagging, Chunking, and Parsing",dependency parsing,nlp engineering experiment|model analysis & interpretability, +3571,Dialogue and Interactive Systems,task-oriented|dialogue state tracking,nlp engineering experiment|position papers, +3572,Machine Translation,few-shot/zero-shot mt|scaling,data analysis, +3576,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",argument mining|applications,nlp engineering experiment|approaches for low-resource settings, +3578,Interpretability and Analysis of Models for NLP,probing,model analysis & interpretability|data resources,spanish|french|portuguese|italian +3579,Computational Social Science and Cultural Analytics,emotion detection and analysis,nlp engineering experiment|approaches for low-resource settings, +3590,Discourse and Pragmatics,coherence,model analysis & interpretability, +3593,Question Answering,table qa,model analysis & interpretability, +3596,Multilingualism and Cross-Lingual NLP,code-switching,nlp engineering experiment|approaches for low-resource settings, +3601,Resources and Evaluation,corpus creation|benchmarking|automatic creation and evaluation of language resources|nlp datasets,data resources|data analysis,korean +3607,Computational Social Science and Cultural Analytics,nlp tools for social analysis|quantiative analyses of news and/or social media,publicly available software and pre-trained models|data resources|data analysis|theory, +3608,Theme Track: Reality Check,(non-)generalizability|evaluation|ai hype & expectations|lessons from other fields,position papers, +3612,Summarization,evaluation,nlp engineering experiment, +3614,"Syntax: Tagging, Chunking, and Parsing",dependency parsing,nlp engineering experiment, +3615,Resources and Evaluation,nlp datasets,data resources, +3616,"Language Grounding to Vision, Robotics, and Beyond",vision language navigation,nlp engineering experiment|model analysis & interpretability, +3622,Theme Track: Reality Check,evaluation|forgotten lessons,data analysis, +3625,NLP Applications,financial/business nlp,nlp engineering experiment|model analysis & interpretability|data resources, +3626,Dialogue and Interactive Systems,applications,nlp engineering experiment, +3627,Machine Learning for NLP,adversarial training,nlp engineering experiment, +3629,Ethics and NLP,model bias/unfairness mitigation,nlp engineering experiment, +3630,Resources and Evaluation,multilingual corpora|nlp datasets,nlp engineering experiment|data resources,french|german|italian|spanish +3633,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",phrase/sentence embedding,nlp engineering experiment, +3636,Interpretability and Analysis of Models for NLP,explanation faithfulness|feature attribution,nlp engineering experiment|model analysis & interpretability, +3637,Theme Track: Reality Check,methodology,position papers, +3638,Dialogue and Interactive Systems,grounded dialog,nlp engineering experiment|data resources|data analysis, +3642,Dialogue and Interactive Systems,applications,nlp engineering experiment, +3649,Dialogue and Interactive Systems,knowledge augmented|applications|grounded dialog,nlp engineering experiment, +3654,NLP Applications,knowledge graphs,"nlp engineering experiment|approaches for low-compute settings, efficiency", +3655,Large Language Models,robustness,nlp engineering experiment|model analysis & interpretability, +3656,Generation,automatic evaluation,nlp engineering experiment, +3659,"Language Grounding to Vision, Robotics, and Beyond",cross-modal content generation,"approaches for low-compute settings, efficiency", +3664,Summarization,factuality,nlp engineering experiment, +3667,Multilingualism and Cross-Lingual NLP,mutlilingual representations,model analysis & interpretability, +3672,NLP Applications,financial/business nlp,publicly available software and pre-trained models,danish|french|german +3673,Machine Translation,automatic evaluation,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models,marathi|nepali|sinhala|german|russian|romanian|estonian +3680,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment, +3682,Multilingualism and Cross-Lingual NLP,multilingual benchmarks|multilingual evaluation,nlp engineering experiment|data resources|data analysis,hindi|indonesian|yoruba|kannada|sundanese|swahili|javanese +3684,Large Language Models,pre-training,"approaches for low-compute settings, efficiency", +3686,Machine Learning for NLP,transfer learning / domain adaptation,"nlp engineering experiment|approaches for low-compute settings, efficiency|approaches for low-resource settings|publicly available software and pre-trained models", +3687,Ethics and NLP,model bias/fairness evaluation|ethical considerations in nlp applications,model analysis & interpretability|reproduction study, +3693,Theme Track: Reality Check,(non-)generalizability|evaluation|methodology,model analysis & interpretability|data analysis|position papers,arabic|german|spanish|swahili|turkish +3698,Question Answering,math qa|table qa,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models, +3699,Summarization,abstractive summarisation|factuality,nlp engineering experiment, +3701,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",phrase/sentence embedding,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency|publicly available software and pre-trained models", +3704,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +3705,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",phrase/sentence embedding|word/phrase alignment,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency|publicly available software and pre-trained models", +3709,Resources and Evaluation,evaluation|reproducibility|statistical testing for evaluation,nlp engineering experiment|data analysis|theory, +3713,Multilingualism and Cross-Lingual NLP,multilingualism|cross-lingual transfer|multilingual evaluation,nlp engineering experiment|approaches for low-resource settings, +3721,Speech and Multimodality,multimodality,nlp engineering experiment, +3724,Machine Learning for NLP,generalization|human-in-the-loop / active learning,nlp engineering experiment, +3725,Machine Learning for NLP,knowledge-augmented methods|multi-task learning|few-shot learning,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency|approaches for low-resource settings", +3729,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training,model analysis & interpretability, +3730,Theme Track: Reality Check,right for the wrong reasons|(non-)generalizability|evaluation,model analysis & interpretability|data resources|data analysis|reproduction study, +3733,Question Answering,knowledge base qa|multihop qa,nlp engineering experiment, +3734,Theme Track: Reality Check,evaluation,data analysis|reproduction study|surveys, +3736,"Syntax: Tagging, Chunking, and Parsing","parsing algorighms (symbolic, theoritical results)",theory, +3738,"Language Grounding to Vision, Robotics, and Beyond",cross-modal matchine translation,nlp engineering experiment, +3750,Information Extraction,named entity recognition and relation extraction|document-level extraction,nlp engineering experiment, +3752,Large Language Models,prompting,nlp engineering experiment, +3754,Semantics: Lexical,lexical relationships,position papers, +3755,Information Retrieval and Text Mining,document representation,nlp engineering experiment, +3757,Machine Learning for NLP,generalization,approaches for low-resource settings|publicly available software and pre-trained models, +3758,Theme Track: Reality Check,evaluation,position papers, +3759,NLP Applications,security/privacy,nlp engineering experiment, +3760,Interpretability and Analysis of Models for NLP,probing|robustness,model analysis & interpretability, +3763,Discourse and Pragmatics,conversation|communication,data resources|data analysis, +3765,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment|approaches for low-resource settings, +3768,Machine Translation,biases|modelling|multimodality,nlp engineering experiment|model analysis & interpretability, +3769,Computational Social Science and Cultural Analytics,human behavior analysis|sociolinguistics,data analysis, +3773,Resources and Evaluation,corpus creation|language resources|nlp datasets|datasets for low resource languages,publicly available software and pre-trained models|data resources,turkish +3774,Summarization,long-form summarization,data resources, +3780,Information Extraction,named entity recognition and relation extraction,approaches for low-resource settings|data analysis, +3782,Semantics: Lexical,lexical semantic change,publicly available software and pre-trained models, +3784,Machine Translation,automatic evaluation,model analysis & interpretability|publicly available software and pre-trained models, +3786,Large Language Models,prompting|robustness,nlp engineering experiment|model analysis & interpretability, +3788,Dialogue and Interactive Systems,embodied agents,model analysis & interpretability|data analysis|theory, +3791,Semantics: Lexical,multi-word expressions|metaphor,nlp engineering experiment|approaches for low-resource settings, +3794,Generation,efficient models,"approaches for low-compute settings, efficiency", +3796,Large Language Models,prompting|scaling,model analysis & interpretability, +3799,Resources and Evaluation,datasets for low resource languages,data resources,bemba +3802,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings|publicly available software and pre-trained models, +3809,Machine Translation,modelling,nlp engineering experiment, +3810,NLP Applications,knowledge graphs,nlp engineering experiment,chinese +3813,"Syntax: Tagging, Chunking, and Parsing",dependency parsing|constituency parsing,nlp engineering experiment|model analysis & interpretability, +3824,Resources and Evaluation,corpus creation|benchmarking|language resources|multilingual corpora|nlp datasets|datasets for low resource languages,data resources|data analysis,hausa +3828,Semantics: Lexical,lexical semantic change,nlp engineering experiment|model analysis & interpretability, +3832,Linguistic Diversity,less-resourced languages|resources for less-resourced languages,approaches for low-resource settings|data resources,acehnese|moroccan arabic|egyptian arabic|bambara|balinese|bhojpuri|banjar|buginese|crimean tatar|southwestern dinka|dzongkha|friulian|nigerian fulfulde|guarani|chhattisgarhi|kashmiri|central kanuri|ligurian|limburgish|lombard|latgalian|magahi|meitei|maori|nuer|dari|southern pashto|sicilian|shan|sardinian|silesian|tamasheq|central atlas tamazight|venetian +3833,Summarization,evaluation,nlp engineering experiment|data resources|data analysis, +3836,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment, +3841,Machine Translation,switch-code translation,data analysis,vietnamese +3843,Theme Track: Reality Check,evaluation|negative results,nlp engineering experiment, +3845,Multilingualism and Cross-Lingual NLP,cross-lingual transfer,nlp engineering experiment|approaches for low-resource settings, +3847,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",textual entailment|natural language inference,"nlp engineering experiment|approaches for low-compute settings, efficiency|approaches for low-resource settings|publicly available software and pre-trained models", +3849,Theme Track: Reality Check,(non-)reproducibility|evaluation|methodology,"model analysis & interpretability|approaches for low-compute settings, efficiency|position papers", +3852,Dialogue and Interactive Systems,conversational modeling,surveys, +3853,NLP Applications,"fact checking, rumour/misinformation detection",approaches for low-resource settings, +3857,Generation,text-to-text generation,nlp engineering experiment|model analysis & interpretability, +3859,Machine Learning for NLP,generalization,model analysis & interpretability, +3860,Interpretability and Analysis of Models for NLP,topic modeling,nlp engineering experiment|model analysis & interpretability, +3864,Theme Track: Reality Check,lessons from deployment,model analysis & interpretability, +3866,Speech and Multimodality,spoken language understanding,approaches for low-resource settings|publicly available software and pre-trained models|data resources|data analysis, +3868,Resources and Evaluation,benchmarking|evaluation methodologies,nlp engineering experiment|model analysis & interpretability, +3876,Semantics: Lexical,polysemy|lexical semantic change|word embeddings|interpretability,nlp engineering experiment|publicly available software and pre-trained models|data analysis, +3878,Interpretability and Analysis of Models for NLP,topic modeling,nlp engineering experiment|publicly available software and pre-trained models|data analysis, +3880,Resources and Evaluation,metrics,nlp engineering experiment, +3883,Large Language Models,pre-training|prompting,nlp engineering experiment|publicly available software and pre-trained models, +3888,Interpretability and Analysis of Models for NLP,human-subject application-grounded evaluations,model analysis & interpretability, +3890,Machine Translation,automatic evaluation|human evaluation,data resources|data analysis,chinese +3893,Ethics and NLP,data ethics,model analysis & interpretability|data analysis, +3895,Multilingualism and Cross-Lingual NLP,cross-lingual transfer|mutlilingual representations,nlp engineering experiment|approaches for low-resource settings, +3900,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment, +3903,Information Extraction,named entity recognition and relation extraction|multilingual extraction,nlp engineering experiment|publicly available software and pre-trained models|data resources, +3907,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training,nlp engineering experiment|model analysis & interpretability|theory, +3908,Large Language Models,prompting|fine-tuning,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings|data resources, +3909,Resources and Evaluation,nlp datasets,model analysis & interpretability|data resources|data analysis, +3911,Interpretability and Analysis of Models for NLP,probing,model analysis & interpretability, +3912,Ethics and NLP,ethical considerations in nlp applications,model analysis & interpretability, +3913,Generation,inference methods,model analysis & interpretability|approaches for low-resource settings, +3917,Computational Social Science and Cultural Analytics,nlp tools for social analysis,data resources, +3919,Ethics and NLP,ethical considerations in nlp applications,nlp engineering experiment, +3921,Question Answering,semantic parsing,nlp engineering experiment, +3923,Computational Social Science and Cultural Analytics,nlp tools for social analysis,nlp engineering experiment, +3924,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models|data resources|data analysis,icelandic +3926,Theme Track: Reality Check,evaluation|methodology|ai hype & expectations|lessons from other fields,position papers|surveys, +3928,Information Extraction,zero/few-shot extraction,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings|publicly available software and pre-trained models, +3930,Dialogue and Interactive Systems,multilingual / low resource,nlp engineering experiment, +3938,Multilingualism and Cross-Lingual NLP,multilingual pre-training,publicly available software and pre-trained models|data resources,hundereds of low-resource languages +3939,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training|robustness,nlp engineering experiment|model analysis & interpretability, +3940,Discourse and Pragmatics,,data analysis, +3944,NLP Applications,historical nlp,nlp engineering experiment,greek +3945,Dialogue and Interactive Systems,evaluation and metrics|task-oriented,nlp engineering experiment, +3948,"Language Grounding to Vision, Robotics, and Beyond",image text matching,nlp engineering experiment|model analysis & interpretability, +3951,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +3953,NLP Applications,"healthcare applications, clincial nlp",nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models|data resources, +3954,Resources and Evaluation,corpus creation|language resources|multilingual corpora|nlp datasets|evaluation|datasets for low resource languages,data resources|data analysis,croatian +3955,"Language Grounding to Vision, Robotics, and Beyond",cross-modal content generation,nlp engineering experiment, +3957,Machine Learning for NLP,contrastive learning,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models, +3958,Summarization,abstractive summarisation|conversational summarization,nlp engineering experiment|approaches for low-resource settings, +3959,Interpretability and Analysis of Models for NLP,explanation faithfulness|feature attribution,model analysis & interpretability, +3960,Generation,automatic evaluation|text-to-text generation,publicly available software and pre-trained models|data resources, +3961,Machine Learning for NLP,generalization|continual learning|meta learning,"nlp engineering experiment|approaches for low-compute settings, efficiency", +3962,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",natural language inference,"approaches for low-compute settings, efficiency", +3963,Resources and Evaluation,corpus creation,data resources, +3969,Ethics and NLP,model bias/fairness evaluation,model analysis & interpretability|data resources, +3970,Semantics: Lexical,multilinguality,theory, +3973,"Language Grounding to Vision, Robotics, and Beyond",cross-modal application,publicly available software and pre-trained models|data resources|data analysis, +3976,Information Extraction,named entity recognition and relation extraction,model analysis & interpretability|publicly available software and pre-trained models, +3977,"Language Grounding to Vision, Robotics, and Beyond",cross-modal pretraining,model analysis & interpretability|reproduction study, +3982,Theme Track: Reality Check,right for the wrong reasons|ai hype & expectations,position papers, +3983,Ethics and NLP,ethical considerations in nlp applications|reflections and critiques,data analysis|position papers, +3985,"Syntax: Tagging, Chunking, and Parsing","semantic parsing|multi-task approaches (large definition)|low-resources languages pos tagging, parsing and related tasks",publicly available software and pre-trained models, +3991,Multilingualism and Cross-Lingual NLP,multilingualism|linguistic variation|multilingual benchmarks|multilingual evaluation,"model analysis & interpretability|approaches for low-compute settings, efficiency|approaches for low-resource settings|data resources|data analysis",french|spanish|italian|russian|slovak|arabic|finnish|swedish|swahili|mandarin chinese|german +3992,Summarization,abstractive summarisation,data resources|data analysis, +3993,Generation,text-to-text generation,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models, +3998,Large Language Models,interpretability/analysis,model analysis & interpretability, +3999,Interpretability and Analysis of Models for NLP,free-text/natural language explanations,model analysis & interpretability, +4005,Dialogue and Interactive Systems,evaluation and metrics|task-oriented|applications|conversational modeling,nlp engineering experiment, +4007,NLP Applications,code generation and understanding,nlp engineering experiment|data resources, +4008,"Syntax: Tagging, Chunking, and Parsing","parsing algorighms (symbolic, theoritical results)",theory, +4012,Interpretability and Analysis of Models for NLP,human-subject application-grounded evaluations,nlp engineering experiment|model analysis & interpretability|data resources, +4013,Large Language Models,fine-tuning,nlp engineering experiment|data resources, +4017,Ethics and NLP,model bias/fairness evaluation,model analysis & interpretability, +4018,Machine Learning for NLP,self-supervised learning|transfer learning / domain adaptation|continual learning,publicly available software and pre-trained models|data resources,french +4020,Interpretability and Analysis of Models for NLP,data influence|hardness of samples,model analysis & interpretability|approaches for low-resource settings|data analysis, +4021,Resources and Evaluation,benchmarking,model analysis & interpretability, +4026,Summarization,abstractive summarisation,publicly available software and pre-trained models, +4028,Resources and Evaluation,corpus creation|language resources|nlp datasets|evaluation,data resources|data analysis, +4032,Ethics and NLP,data ethics|human factors in nlp|ethical considerations in nlp applications,"approaches for low-compute settings, efficiency|approaches for low-resource settings|data resources",hindi +4037,Discourse and Pragmatics,argument mining,nlp engineering experiment,russian +4039,Resources and Evaluation,nlp datasets,data resources|data analysis, +4044,Machine Translation,scaling,"model analysis & interpretability|approaches for low-compute settings, efficiency|approaches for low-resource settings",german|russian|spanish|french +4049,Information Extraction,entity linking/disambiguation,nlp engineering experiment|model analysis & interpretability, +4051,Interpretability and Analysis of Models for NLP,topic modeling,nlp engineering experiment, +4053,Computational Social Science and Cultural Analytics,emoji prediction and analysis,nlp engineering experiment, +4055,Interpretability and Analysis of Models for NLP,counterfactual/contrastive explanations|feature attribution,nlp engineering experiment|model analysis & interpretability, +4056,Ethics and NLP,model bias/fairness evaluation|reflections and critiques,position papers, +4058,Semantics: Lexical,metaphor,model analysis & interpretability, +4062,Semantics: Lexical,lexical relationships|compositionality|multi-word expressions|paraphrasing,nlp engineering experiment|data resources|data analysis, +4064,Theme Track: Reality Check,lessons from other fields,model analysis & interpretability, +4065,Multilingualism and Cross-Lingual NLP,linguistic variation|multilingual pre-training|dialects and language varieties,"nlp engineering experiment|approaches for low-compute settings, efficiency|position papers", +4069,Discourse and Pragmatics,dialogue|conversation,nlp engineering experiment|model analysis & interpretability|data resources|data analysis, +4074,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training|robustness,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency", +4079,Discourse and Pragmatics,bridging resolution,nlp engineering experiment, +4080,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",linguistic theories|cognitive modeling|computational psycholinguistics,model analysis & interpretability|data analysis,dutch +4086,Discourse and Pragmatics,,nlp engineering experiment|data resources|data analysis, +4087,Resources and Evaluation,corpus creation|multilingual corpora|nlp datasets|datasets for low resource languages,data resources,assamese|bhojpuri|bengali|gujarati|hindi|kannada|malayalam|marathi|nepali|oriya|punjabi|tamil|telugu|urdu +4089,"Phonology, Morphology, and Word Segmentation",morphological inflection|phonology|grapheme-to-phoneme conversion,nlp engineering experiment, +4090,NLP Applications,mathematical nlp,nlp engineering experiment, +4091,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",textual entailment|natural language inference,data resources|data analysis, +4101,Theme Track: Reality Check,ai hype & expectations|forgotten lessons,data analysis|position papers, +4105,Question Answering,open-domain qa,model analysis & interpretability|publicly available software and pre-trained models, +4106,Semantics: Lexical,,approaches for low-resource settings, +4115,Resources and Evaluation,corpus creation|benchmarking|nlp datasets,publicly available software and pre-trained models|data resources|data analysis,german +4116,Computational Social Science and Cultural Analytics,human-computer interaction|nlp tools for social analysis,nlp engineering experiment|data resources|data analysis, +4118,Machine Learning for NLP,representation learning,model analysis & interpretability, +4124,Machine Translation,biases|efficient mt training|pre-training for mt,nlp engineering experiment|approaches for low-resource settings|data analysis,german|burmese|indonesian|turkish|tagalog +4125,Summarization,abstractive summarisation,nlp engineering experiment, +4130,Computational Social Science and Cultural Analytics,misinformation detection and analysis,nlp engineering experiment|approaches for low-resource settings, +4132,Large Language Models,pre-training,"nlp engineering experiment|approaches for low-compute settings, efficiency", +4135,Computational Social Science and Cultural Analytics,sociolinguistics|nlp tools for social analysis,data analysis, +4136,Generation,analysis,model analysis & interpretability, +4139,"Phonology, Morphology, and Word Segmentation",subword representations,nlp engineering experiment, +4140,"Language Grounding to Vision, Robotics, and Beyond",cross-modal pretraining|image text matching|cross-modal application,nlp engineering experiment|approaches for low-resource settings, +4152,"Syntax: Tagging, Chunking, and Parsing",,nlp engineering experiment, +4153,Machine Translation,speech translation,"nlp engineering experiment|approaches for low-compute settings, efficiency", +4155,Generation,efficient models,"approaches for low-compute settings, efficiency", +4157,Generation,inference methods,nlp engineering experiment, +4158,"Language Grounding to Vision, Robotics, and Beyond",cross-modal content generation,nlp engineering experiment, +4159,Machine Learning for NLP,generalization,theory, +4162,"Language Grounding to Vision, Robotics, and Beyond",cross-modal application,publicly available software and pre-trained models, +4171,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",cognitive modeling,model analysis & interpretability,french +4176,Large Language Models,applications,publicly available software and pre-trained models, +4177,Resources and Evaluation,corpus creation|reproducibility,"approaches for low-compute settings, efficiency",french +4178,Generation,efficient models|model architectures,"approaches for low-compute settings, efficiency", +4181,Information Extraction,event extraction|zero/few-shot extraction,nlp engineering experiment|approaches for low-resource settings, +4182,Information Extraction,event extraction,nlp engineering experiment|model analysis & interpretability, +4186,Generation,human evaluation|data-to-text generation|text-to-text generation,data resources|data analysis, +4188,Computational Social Science and Cultural Analytics,nlp tools for social analysis,data resources|data analysis, +4192,Summarization,factuality,nlp engineering experiment|data resources, +4194,Resources and Evaluation,benchmarking,model analysis & interpretability|reproduction study, +4196,Information Extraction,named entity recognition and relation extraction|zero/few-shot extraction,"approaches for low-compute settings, efficiency|approaches for low-resource settings", +4198,"Syntax: Tagging, Chunking, and Parsing","parsing algorighms (symbolic, theoritical results)",data analysis, +4202,"Syntax: Tagging, Chunking, and Parsing","constituency parsing|parsing algorighms (symbolic, theoritical results)",publicly available software and pre-trained models|theory, +4207,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +4208,Linguistic Diversity,less-resourced languages,approaches for low-resource settings,azeri turkish|mazanderani|gilaki|sindhi|kashmiri|central kurdish|northern kurdish|gorani|persian|arabic|urdu +4209,Question Answering,commonsense qa,nlp engineering experiment, +4210,Information Extraction,knowledge base construction,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models|data resources|data analysis|reproduction study, +4213,"Language Grounding to Vision, Robotics, and Beyond",cross-modal pretraining,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models|data resources, +4215,Information Extraction,open information extraction,nlp engineering experiment, +4220,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training,model analysis & interpretability, +4221,Information Retrieval and Text Mining,dense retrieval,nlp engineering experiment, +4225,Summarization,abstractive summarisation|long-form summarization,nlp engineering experiment, +4226,Information Extraction,event extraction,nlp engineering experiment, +4228,Question Answering,semantic parsing|interpretability|reasoning|math qa,model analysis & interpretability|data resources, +4230,Summarization,,nlp engineering experiment, +4233,Large Language Models,applications,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings|data resources, +4234,Interpretability and Analysis of Models for NLP,free-text/natural language explanations,nlp engineering experiment|model analysis & interpretability, +4235,Ethics and NLP,ethical considerations in nlp applications,position papers, +4238,Large Language Models,continual learning,nlp engineering experiment, +4242,Large Language Models,prompting,"nlp engineering experiment|approaches for low-compute settings, efficiency", +4247,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",linguistic theories,model analysis & interpretability, +4250,Machine Translation,online adaptation for mt,nlp engineering experiment|approaches for low-resource settings, +4252,Computational Social Science and Cultural Analytics,human behavior analysis|sociolinguistics,nlp engineering experiment|data resources, +4253,Resources and Evaluation,nlp datasets,data resources, +4254,Machine Learning for NLP,few-shot learning|meta learning,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency|approaches for low-resource settings", +4258,Large Language Models,applications,nlp engineering experiment|model analysis & interpretability, +4260,Computational Social Science and Cultural Analytics,hate-speech detection|sociolinguistics|quantiative analyses of news and/or social media,data resources|data analysis|theory, +4263,Machine Learning for NLP,,"approaches for low-compute settings, efficiency|approaches for low-resource settings", +4264,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",computational psycholinguistics,data analysis, +4268,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment, +4277,NLP Applications,"healthcare applications, clincial nlp",publicly available software and pre-trained models, +4280,Summarization,extractive summarisation,approaches for low-resource settings|data resources, +4282,Computational Social Science and Cultural Analytics,human behavior analysis,data resources|data analysis, +4286,Large Language Models,interpretability/analysis,model analysis & interpretability, +4289,Dialogue and Interactive Systems,interactive storytelling|applications|grounded dialog|conversational modeling,nlp engineering experiment|publicly available software and pre-trained models|data resources, +4295,Discourse and Pragmatics,anaphora resolution|coreference resolution|bridging resolution,data resources|position papers|theory, +4296,Theme Track: Reality Check,(non-)reproducibility,data analysis|reproduction study, +4297,Theme Track: Reality Check,(non-)generalizability|ai hype & expectations,data analysis,french +4308,Semantics: Lexical,compositionality|word embeddings,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency|approaches for low-resource settings|publicly available software and pre-trained models|data resources", +4312,Large Language Models,security and privacy|fine-tuning,"nlp engineering experiment|approaches for low-compute settings, efficiency|approaches for low-resource settings", +4315,Linguistic Diversity,less-resourced languages|endangered languages|indigenous languages,model analysis & interpretability|approaches for low-resource settings|data analysis, +4317,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +4324,NLP Applications,"fact checking, rumour/misinformation detection",nlp engineering experiment, +4326,NLP Applications,knowledge graphs,"nlp engineering experiment|approaches for low-compute settings, efficiency|approaches for low-resource settings|publicly available software and pre-trained models", +4328,Information Extraction,named entity recognition and relation extraction|zero/few-shot extraction,"nlp engineering experiment|approaches for low-compute settings, efficiency", +4330,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",cognitive modeling,surveys, +4334,Information Retrieval and Text Mining,passage retrieval,"approaches for low-compute settings, efficiency", +4335,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",stance detection,data resources,french|german|portuguese|dutch|romanian +4336,Resources and Evaluation,benchmarking,model analysis & interpretability|publicly available software and pre-trained models|data resources|data analysis, +4340,Interpretability and Analysis of Models for NLP,free-text/natural language explanations|human-subject application-grounded evaluations,model analysis & interpretability|data resources, +4341,NLP Applications,multimodal applications,nlp engineering experiment, +4348,Generation,domain adaptation,nlp engineering experiment, +4350,"Syntax: Tagging, Chunking, and Parsing",semantic parsing,nlp engineering experiment, +4357,Question Answering,conversational qa,nlp engineering experiment|data resources, +4371,Machine Learning for NLP,graph-based methods|continual learning,nlp engineering experiment, +4372,Information Extraction,named entity recognition and relation extraction|event extraction,nlp engineering experiment, +4374,Theme Track: Reality Check,evaluation,model analysis & interpretability|publicly available software and pre-trained models|data analysis, +4382,Dialogue and Interactive Systems,knowledge augmented|commonsense reasoning,publicly available software and pre-trained models|data resources|data analysis, +4384,Large Language Models,prompting,"approaches for low-compute settings, efficiency", +4387,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",paraphrase generation,"approaches for low-compute settings, efficiency", +4395,Linguistic Diversity,less-resourced languages|endangered languages|indigenous languages|minoritized languages|language documentation|resources for less-resourced languages|software and tools,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency|approaches for low-resource settings|publicly available software and pre-trained models|data resources|data analysis",517 african languages|language varieties +4396,Ethics and NLP,model bias/fairness evaluation,data resources, +4398,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",cognitive modeling,theory, +4404,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",applications,nlp engineering experiment, +4406,Information Extraction,event extraction|document-level extraction|zero/few-shot extraction,approaches for low-resource settings|data resources|data analysis, +4408,NLP Applications,mathematical nlp,theory, +4409,NLP Applications,"healthcare applications, clincial nlp",model analysis & interpretability, +4415,Machine Learning for NLP,reinforcement learning,nlp engineering experiment, +4416,Generation,text-to-text generation,nlp engineering experiment|approaches for low-resource settings, +4419,Dialogue and Interactive Systems,spoken dialogue systems|task-oriented|applications,nlp engineering experiment|publicly available software and pre-trained models|data resources|data analysis, +4422,Computational Social Science and Cultural Analytics,nlp tools for social analysis,nlp engineering experiment|data analysis, +4423,Theme Track: Reality Check,evaluation|ai hype & expectations,model analysis & interpretability, +4424,Question Answering,multihop qa|reasoning|few-shot qa|open-domain qa,nlp engineering experiment|approaches for low-resource settings, +4433,Resources and Evaluation,evaluation methodologies,model analysis & interpretability|data resources, +4435,Question Answering,conversational qa,nlp engineering experiment|data analysis, +4439,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",argument mining,nlp engineering experiment, +4442,Machine Learning for NLP,generalization,nlp engineering experiment|model analysis & interpretability, +4446,Machine Learning for NLP,data augmentation|generalization|human-in-the-loop / active learning,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models|data analysis, +4448,Summarization,abstractive summarisation|conversational summarization|long-form summarization|architectures,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models, +4450,NLP Applications,"fact checking, rumour/misinformation detection",data resources, +4451,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +4452,Information Extraction,event extraction,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings, +4456,Computational Social Science and Cultural Analytics,nlp tools for social analysis,data analysis, +4462,NLP Applications,"healthcare applications, clincial nlp",nlp engineering experiment|approaches for low-resource settings, +4463,Computational Social Science and Cultural Analytics,language/cultural bias analysis,data analysis, +4466,"Language Grounding to Vision, Robotics, and Beyond",cross-modal content generation,nlp engineering experiment, +4470,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training,model analysis & interpretability, +4471,Machine Translation,multilingual mt,"nlp engineering experiment|approaches for low-compute settings, efficiency|approaches for low-resource settings", +4481,Information Extraction,event extraction,approaches for low-resource settings|data resources, +4482,Machine Learning for NLP,contrastive learning,approaches for low-resource settings, +4484,Dialogue and Interactive Systems,evaluation and metrics,data resources|data analysis, +4485,Dialogue and Interactive Systems,retrieval|applications|conversational modeling,nlp engineering experiment, +4488,Interpretability and Analysis of Models for NLP,data shortcuts/artifacts|robustness,model analysis & interpretability|data analysis, +4490,Information Extraction,named entity recognition and relation extraction,model analysis & interpretability, +4494,Generation,inference methods,nlp engineering experiment|theory, +4495,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",textual entailment|natural language inference|reasoning,nlp engineering experiment|data resources, +4497,Multilingualism and Cross-Lingual NLP,multilingual pre-training,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency|approaches for low-resource settings|publicly available software and pre-trained models", +4500,Computational Social Science and Cultural Analytics,nlp tools for social analysis,data resources|data analysis, +4501,Question Answering,multihop qa|interpretability|reasoning,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models, +4503,Large Language Models,scaling|retrieval-augmented models|robustness,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings|data resources, +4505,Speech and Multimodality,video processing|multimodality,nlp engineering experiment|model analysis & interpretability, +4509,Information Retrieval and Text Mining,contrastive learning,nlp engineering experiment, +4513,Large Language Models,prompting,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings, +4517,Ethics and NLP,model bias/fairness evaluation|model bias/unfairness mitigation|participatory/community-based nlp|ethical considerations in nlp applications,model analysis & interpretability|publicly available software and pre-trained models|data resources, +4519,Information Extraction,named entity recognition and relation extraction|event extraction,nlp engineering experiment|model analysis & interpretability, +4521,Summarization,multi-document summarization|evaluation,data resources|data analysis, +4522,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",textual entailment,model analysis & interpretability|data resources, +4524,Large Language Models,prompting|retrieval-augmented models|fine-tuning,"nlp engineering experiment|approaches for low-compute settings, efficiency|approaches for low-resource settings", +4525,Interpretability and Analysis of Models for NLP,free-text/natural language explanations,model analysis & interpretability, +4526,Resources and Evaluation,benchmarking|nlp datasets,model analysis & interpretability|data resources, +4529,Large Language Models,prompting|scaling|fine-tuning,"nlp engineering experiment|approaches for low-compute settings, efficiency", +4532,Machine Translation,efficient mt training|few-shot/zero-shot mt,"approaches for low-compute settings, efficiency|approaches for low-resource settings", +4536,Machine Learning for NLP,transfer learning / domain adaptation,approaches for low-resource settings, +4541,Large Language Models,,model analysis & interpretability, +4543,Large Language Models,pre-training|scaling|fine-tuning,"nlp engineering experiment|approaches for low-compute settings, efficiency", +4544,Theme Track: Reality Check,evaluation,model analysis & interpretability, +4549,"Language Grounding to Vision, Robotics, and Beyond",cross-modal content generation,model analysis & interpretability|data resources|data analysis,spanish|german|chinese|japanese|hebrew|indonesian +4552,Information Retrieval and Text Mining,passage retrieval|dense retrieval|re-ranking,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models|data resources, +4553,Information Extraction,open information extraction,publicly available software and pre-trained models|data analysis|surveys, +4557,NLP Applications,knowledge graphs,nlp engineering experiment, +4558,Information Extraction,event extraction,nlp engineering experiment|approaches for low-resource settings, +4559,Large Language Models,interpretability/analysis|robustness|fine-tuning,nlp engineering experiment|model analysis & interpretability, +4565,Resources and Evaluation,language resources|nlp datasets,nlp engineering experiment|data resources, +4570,NLP Applications,,nlp engineering experiment|model analysis & interpretability, +4577,Resources and Evaluation,corpus creation|nlp datasets,model analysis & interpretability|data resources, +4581,Resources and Evaluation,evaluation methodologies|evaluation,data analysis, +4582,Ethics and NLP,model bias/fairness evaluation|human factors in nlp,model analysis & interpretability, +4584,Machine Translation,few-shot/zero-shot mt,data analysis, +4585,Dialogue and Interactive Systems,retrieval,"nlp engineering experiment|approaches for low-compute settings, efficiency", +4590,NLP Applications,financial/business nlp,model analysis & interpretability, +4593,Resources and Evaluation,metrics,model analysis & interpretability, +4598,Information Extraction,open information extraction,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models, +4605,NLP Applications,"fact checking, rumour/misinformation detection",nlp engineering experiment|publicly available software and pre-trained models|data resources, +4613,"Syntax: Tagging, Chunking, and Parsing",grammar and knowledge-based approaches,nlp engineering experiment, +4614,Generation,efficient models,"approaches for low-compute settings, efficiency", +4619,Theme Track: Reality Check,(non-)reproducibility,data analysis|position papers|surveys, +4623,Large Language Models,pre-training,nlp engineering experiment, +4627,Summarization,extractive summarisation,nlp engineering experiment|approaches for low-resource settings, +4634,Summarization,evaluation,"model analysis & interpretability|approaches for low-compute settings, efficiency|approaches for low-resource settings|data resources|data analysis", +4635,Generation,text-to-text generation,publicly available software and pre-trained models, +4641,Large Language Models,pre-training,"nlp engineering experiment|approaches for low-compute settings, efficiency", +4660,Question Answering,multihop qa,nlp engineering experiment, +4662,NLP Applications,code generation and understanding,nlp engineering experiment, +4667,Information Extraction,named entity recognition and relation extraction|document-level extraction,nlp engineering experiment, +4669,Speech and Multimodality,multimodality,model analysis & interpretability, +4674,NLP Applications,financial/business nlp,model analysis & interpretability, +4675,Theme Track: Reality Check,evaluation,data resources, +4679,Speech and Multimodality,automatic speech recognition|speech technologies|multimodality,nlp engineering experiment|approaches for low-resource settings, +4680,Ethics and NLP,model bias/fairness evaluation|ethical considerations in nlp applications,nlp engineering experiment|model analysis & interpretability|data analysis, +4681,Question Answering,open-domain qa,nlp engineering experiment, +4682,Resources and Evaluation,corpus creation|multilingual corpora|nlp datasets|datasets for low resource languages,nlp engineering experiment|approaches for low-resource settings|data resources|data analysis, +4683,Machine Learning for NLP,generative models,nlp engineering experiment, +4687,Information Retrieval and Text Mining,contrastive learning,nlp engineering experiment|data resources, +4689,"Language Grounding to Vision, Robotics, and Beyond",cross-modal application|cross-modal information extraction,nlp engineering experiment|model analysis & interpretability|theory, +4690,Interpretability and Analysis of Models for NLP,explanation faithfulness|hardness of samples,nlp engineering experiment|model analysis & interpretability, +4691,Information Extraction,named entity recognition and relation extraction|event extraction,nlp engineering experiment|data resources,engilish|japanese +4694,Machine Learning for NLP,knowledge-augmented methods,nlp engineering experiment, +4704,Question Answering,multihop qa,nlp engineering experiment|model analysis & interpretability, +4706,Dialogue and Interactive Systems,task-oriented|dialogue state tracking,nlp engineering experiment, +4709,Machine Translation,multilingual mt,nlp engineering experiment, +4711,Machine Learning for NLP,transfer learning / domain adaptation,nlp engineering experiment|publicly available software and pre-trained models, +4715,Large Language Models,prompting,nlp engineering experiment, +4717,Information Retrieval and Text Mining,re-ranking,"nlp engineering experiment|approaches for low-compute settings, efficiency", +4718,NLP Applications,"fact checking, rumour/misinformation detection",nlp engineering experiment, +4721,Information Extraction,named entity recognition and relation extraction,approaches for low-resource settings, +4722,Question Answering,open-domain qa,model analysis & interpretability|data resources, +4724,"Phonology, Morphology, and Word Segmentation",morphological inflection,approaches for low-resource settings, +4725,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training,model analysis & interpretability, +4728,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",cognitive modeling|computational psycholinguistics,nlp engineering experiment|theory, +4729,Ethics and NLP,model bias/fairness evaluation,model analysis & interpretability, +4730,Dialogue and Interactive Systems,task-oriented,nlp engineering experiment,chinese +4733,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",argument mining|stance detection|argument schemes and reasoning,nlp engineering experiment|data resources|data analysis, +4740,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",applications,nlp engineering experiment, +4744,Discourse and Pragmatics,discourse relations,nlp engineering experiment|data resources, +4745,Summarization,extractive summarisation|abstractive summarisation|query-focused summarization|evaluation,nlp engineering experiment|data resources|data analysis, +4746,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings, +4752,Generation,model architectures,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings|data analysis|theory, +4763,Large Language Models,interpretability/analysis,model analysis & interpretability, +4766,Large Language Models,prompting,nlp engineering experiment, +4769,Machine Learning for NLP,self-supervised learning|contrastive learning|representation learning,nlp engineering experiment|publicly available software and pre-trained models, +4772,Discourse and Pragmatics,coreference resolution,nlp engineering experiment, +4776,Resources and Evaluation,evaluation methodologies|evaluation|metrics,nlp engineering experiment|data resources|data analysis, +4780,"Phonology, Morphology, and Word Segmentation",subword representations,nlp engineering experiment,norwegian|czech|chinese|turkish|arabic +4782,Machine Translation,speech translation,nlp engineering experiment, +4792,Large Language Models,continual learning|fine-tuning,model analysis & interpretability, +4803,Dialogue and Interactive Systems,evaluation and metrics,nlp engineering experiment|data resources, +4810,Resources and Evaluation,benchmarking,data resources, +4811,Interpretability and Analysis of Models for NLP,data shortcuts/artifacts|robustness,nlp engineering experiment|model analysis & interpretability, +4813,Discourse and Pragmatics,discourse parsing,nlp engineering experiment, +4814,"Language Grounding to Vision, Robotics, and Beyond",vision question answering,nlp engineering experiment|approaches for low-resource settings, +4820,"Language Grounding to Vision, Robotics, and Beyond",vision question answering,nlp engineering experiment|approaches for low-resource settings, +4821,Linguistic Diversity,indigenous languages,position papers,indigenous languages of the americas +4823,Generation,efficient models|data-to-text generation|text-to-text generation|inference methods,nlp engineering experiment|model analysis & interpretability, +4824,Information Extraction,event extraction|document-level extraction,nlp engineering experiment|reproduction study, +4825,Resources and Evaluation,corpus creation,data resources, +4826,Resources and Evaluation,corpus creation|benchmarking|language resources|automatic creation and evaluation of language resources|nlp datasets|datasets for low resource languages,approaches for low-resource settings|publicly available software and pre-trained models|data resources,hindi|bengali|assamese|gujarati|kannada|malayalam|marathi|tamil|telugu|punjabi|odia +4827,Question Answering,open-domain qa,nlp engineering experiment, +4835,Multilingualism and Cross-Lingual NLP,cross-lingual transfer,model analysis & interpretability|theory, +4838,Generation,automatic evaluation,nlp engineering experiment, +4839,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +4841,Resources and Evaluation,benchmarking|evaluation,nlp engineering experiment|model analysis & interpretability|data analysis, +4844,Ethics and NLP,model bias/unfairness mitigation,nlp engineering experiment|model analysis & interpretability, +4850,Generation,text-to-text generation,nlp engineering experiment, +4858,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",phrase/sentence embedding,"approaches for low-compute settings, efficiency|publicly available software and pre-trained models", +4865,Generation,data-to-text generation,nlp engineering experiment|approaches for low-resource settings, +4867,Interpretability and Analysis of Models for NLP,calibration/uncertainty,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency", +4870,Generation,text-to-text generation|inference methods|model architectures,nlp engineering experiment|model analysis & interpretability, +4871,Resources and Evaluation,automatic creation and evaluation of language resources,data resources, +4872,Large Language Models,prompting,nlp engineering experiment|model analysis & interpretability, +4877,Interpretability and Analysis of Models for NLP,data influence|hardness of samples,model analysis & interpretability, +4878,Interpretability and Analysis of Models for NLP,data influence|feature attribution|knowledge tracing/discovering/inducing,nlp engineering experiment|publicly available software and pre-trained models|data analysis, +4884,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",applications,nlp engineering experiment, +4888,Information Retrieval and Text Mining,re-ranking,nlp engineering experiment, +4889,"Language Grounding to Vision, Robotics, and Beyond",cross-modal application|cross-modal information extraction,nlp engineering experiment|publicly available software and pre-trained models|data resources, +4891,Resources and Evaluation,corpus creation|benchmarking|nlp datasets,nlp engineering experiment|publicly available software and pre-trained models|data resources, +4899,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",phrase/sentence embedding,"nlp engineering experiment|model analysis & interpretability|approaches for low-compute settings, efficiency|approaches for low-resource settings|publicly available software and pre-trained models", +4901,Information Retrieval and Text Mining,passage retrieval|dense retrieval|pre-training|contrastive learning,nlp engineering experiment|publicly available software and pre-trained models, +4904,Machine Translation,few-shot/zero-shot mt,nlp engineering experiment, +4907,NLP Applications,"educational applications, gec, essay scoring",data resources|data analysis,chinese +4908,Resources and Evaluation,evaluation,data analysis, +4914,Ethics and NLP,model bias/unfairness mitigation,nlp engineering experiment, +4915,Information Extraction,named entity recognition and relation extraction|zero/few-shot extraction,nlp engineering experiment|approaches for low-resource settings, +4916,Large Language Models,prompting|interpretability/analysis,model analysis & interpretability, +4921,Machine Learning for NLP,word embeddings|representation learning,nlp engineering experiment|model analysis & interpretability|theory, +4923,Computational Social Science and Cultural Analytics,human behavior analysis|hate-speech detection|quantiative analyses of news and/or social media,nlp engineering experiment|publicly available software and pre-trained models|data resources, +4936,Large Language Models,interpretability/analysis,nlp engineering experiment, +4937,Ethics and NLP,model bias/fairness evaluation,model analysis & interpretability, +4947,Information Retrieval and Text Mining,,nlp engineering experiment, +4950,Generation,data-to-text generation,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings, +4953,"Language Grounding to Vision, Robotics, and Beyond",cross-modal application,nlp engineering experiment, +4956,Machine Learning for NLP,transfer learning / domain adaptation,nlp engineering experiment|data resources, +4957,Dialogue and Interactive Systems,task-oriented,nlp engineering experiment, +4959,NLP Applications,"healthcare applications, clincial nlp",model analysis & interpretability, +4964,Multilingualism and Cross-Lingual NLP,cross-lingual transfer,approaches for low-resource settings, +4966,Machine Translation,speech translation,nlp engineering experiment, +4967,Question Answering,commonsense qa,nlp engineering experiment, +4968,Computational Social Science and Cultural Analytics,hate-speech detection,nlp engineering experiment|data resources, +4974,Generation,efficient models,nlp engineering experiment, +4975,Interpretability and Analysis of Models for NLP,data influence,nlp engineering experiment|model analysis & interpretability|data analysis, +4980,Information Extraction,named entity recognition and relation extraction,approaches for low-resource settings, +4985,Dialogue and Interactive Systems,spoken dialogue systems,nlp engineering experiment, +4993,Large Language Models,interpretability/analysis,model analysis & interpretability, +4998,Interpretability and Analysis of Models for NLP,probing,model analysis & interpretability, +5002,Large Language Models,fine-tuning,"nlp engineering experiment|approaches for low-compute settings, efficiency|approaches for low-resource settings", +5007,Summarization,multilingual summarisation,nlp engineering experiment|approaches for low-resource settings, +5014,Multilingualism and Cross-Lingual NLP,multilingualism,approaches for low-resource settings,bengali|hindi +5015,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment, +5017,Information Extraction,event extraction,model analysis & interpretability, +5021,NLP Applications,financial/business nlp,nlp engineering experiment|approaches for low-resource settings, +5024,Computational Social Science and Cultural Analytics,language/cultural bias analysis,nlp engineering experiment, +5026,Information Extraction,document-level extraction,nlp engineering experiment, +5027,"Syntax: Tagging, Chunking, and Parsing",dependency parsing,nlp engineering experiment, +5031,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",style analysis|style generation,data analysis, +5041,NLP Applications,code generation and understanding,model analysis & interpretability, +5043,Information Extraction,named entity recognition and relation extraction|open information extraction|zero/few-shot extraction,nlp engineering experiment, +5048,NLP Applications,"educational applications, gec, essay scoring",approaches for low-resource settings, +5049,Multilingualism and Cross-Lingual NLP,mutlilingual representations,nlp engineering experiment|approaches for low-resource settings, +5057,Question Answering,open-domain qa,nlp engineering experiment, +5058,Interpretability and Analysis of Models for NLP,robustness,nlp engineering experiment, +5059,Computational Social Science and Cultural Analytics,sociolinguistics,nlp engineering experiment,mandarin chinese +5065,Interpretability and Analysis of Models for NLP,calibration/uncertainty,nlp engineering experiment, +5071,Computational Social Science and Cultural Analytics,human behavior analysis,data resources|theory, +5079,Resources and Evaluation,nlp datasets,data resources, +5086,Interpretability and Analysis of Models for NLP,knowledge tracing/discovering/inducing,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings, +5090,Interpretability and Analysis of Models for NLP,robustness,position papers, +5095,Multilingualism and Cross-Lingual NLP,mutlilingual representations,nlp engineering experiment|model analysis & interpretability, +5097,Question Answering,reading comprehension|logical reasoning|generalization|reasoning,nlp engineering experiment|publicly available software and pre-trained models|data resources|data analysis, +5099,NLP Applications,multimodal applications,nlp engineering experiment, +5104,Generation,domain adaptation,nlp engineering experiment|approaches for low-resource settings, +5106,Machine Translation,domain adaptation,"approaches for low-compute settings, efficiency", +5109,Machine Learning for NLP,transfer learning / domain adaptation,approaches for low-resource settings, +5112,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",,"approaches for low-compute settings, efficiency", +5113,Theme Track: Reality Check,evaluation,position papers, +5125,Resources and Evaluation,corpus creation,data resources,sign language +5126,Machine Learning for NLP,few-shot learning,approaches for low-resource settings, +5135,Large Language Models,pre-training,"nlp engineering experiment|approaches for low-compute settings, efficiency|publicly available software and pre-trained models", +5140,"Syntax: Tagging, Chunking, and Parsing",dependency parsing,theory, +5141,Large Language Models,continual learning,nlp engineering experiment, +5150,Question Answering,reading comprehension,data resources, +5151,Theme Track: Reality Check,lessons from other fields,position papers|surveys, +5157,"Language Grounding to Vision, Robotics, and Beyond",cross-modal information extraction,approaches for low-resource settings, +5159,Speech and Multimodality,multimodality,model analysis & interpretability, +5166,Generation,automatic evaluation,nlp engineering experiment, +5167,NLP Applications,"healthcare applications, clincial nlp",nlp engineering experiment, +5168,Information Extraction,,approaches for low-resource settings, +5170,Dialogue and Interactive Systems,spoken dialogue systems,nlp engineering experiment, +5171,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",argument generation,approaches for low-resource settings|publicly available software and pre-trained models|data resources, +5176,"Syntax: Tagging, Chunking, and Parsing",semantic parsing,data resources,chinese +5178,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment|model analysis & interpretability, +5183,Machine Translation,parallel decoding/non-autoregressive mt,nlp engineering experiment, +5186,Large Language Models,applications,"approaches for low-compute settings, efficiency", +5188,NLP Applications,financial/business nlp,data resources, +5193,Speech and Multimodality,multimodality,"nlp engineering experiment|approaches for low-compute settings, efficiency", +5204,Machine Translation,automatic evaluation|online adaptation for mt,nlp engineering experiment|approaches for low-resource settings, +5206,Generation,automatic evaluation,nlp engineering experiment|publicly available software and pre-trained models, +5213,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training,"approaches for low-compute settings, efficiency|approaches for low-resource settings", +5217,Question Answering,open-domain qa,nlp engineering experiment, +5227,Multilingualism and Cross-Lingual NLP,cross-lingual transfer,"nlp engineering experiment|approaches for low-compute settings, efficiency", +5229,NLP Applications,"healthcare applications, clincial nlp|knowledge graphs",nlp engineering experiment,chinese +5234,Dialogue and Interactive Systems,factuality|knowledge augmented|grounded dialog,nlp engineering experiment|reproduction study, +5238,Multilingualism and Cross-Lingual NLP,cross-lingual transfer,nlp engineering experiment|approaches for low-resource settings, +5241,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",argument mining,nlp engineering experiment, +5243,Machine Learning for NLP,few-shot learning,approaches for low-resource settings, +5246,Dialogue and Interactive Systems,grounded dialog,publicly available software and pre-trained models, +5248,Generation,efficient models|text-to-text generation,"approaches for low-compute settings, efficiency", +5252,Question Answering,multimodal qa,model analysis & interpretability, +5254,Dialogue and Interactive Systems,applications,nlp engineering experiment|model analysis & interpretability, +5255,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",reasoning,nlp engineering experiment, +5256,"Syntax: Tagging, Chunking, and Parsing",deep syntax parsing,nlp engineering experiment|theory, +5258,Machine Learning for NLP,representation learning,data analysis, +5261,Discourse and Pragmatics,discourse relations,model analysis & interpretability, +5266,Generation,text-to-text generation,nlp engineering experiment, +5267,Generation,inference methods,nlp engineering experiment, +5272,Resources and Evaluation,multilingual corpora,data resources,italian|french|german|polish|russian +5277,Speech and Multimodality,automatic speech recognition|spoken language translation,nlp engineering experiment, +5287,Resources and Evaluation,corpus creation|benchmarking|nlp datasets,nlp engineering experiment|publicly available software and pre-trained models|data resources|data analysis, +5289,Interpretability and Analysis of Models for NLP,probing,model analysis & interpretability, +5293,Dialogue and Interactive Systems,dialogue state tracking,nlp engineering experiment, +5297,Multilingualism and Cross-Lingual NLP,cross-lingual transfer,approaches for low-resource settings, +5312,Large Language Models,prompting|scaling|interpretability/analysis,nlp engineering experiment|model analysis & interpretability, +5316,Large Language Models,prompting,nlp engineering experiment, +5319,"Phonology, Morphology, and Word Segmentation",phonology,model analysis & interpretability, +5325,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",textual entailment,nlp engineering experiment, +5335,"Language Grounding to Vision, Robotics, and Beyond",image text matching,model analysis & interpretability|data resources, +5344,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment, +5345,Question Answering,open-domain qa,model analysis & interpretability, +5352,Dialogue and Interactive Systems,task-oriented|applications,nlp engineering experiment|data resources, +5354,Generation,analysis,model analysis & interpretability, +5357,Multilingualism and Cross-Lingual NLP,multilingualism,approaches for low-resource settings,hindi|marathi|bengali +5362,"Syntax: Tagging, Chunking, and Parsing","parsing algorighms (symbolic, theoritical results)",theory, +5374,Computational Social Science and Cultural Analytics,frame detection and analysis|nlp tools for social analysis,nlp engineering experiment,italian +5378,Interpretability and Analysis of Models for NLP,hierarchical & concept explanations,model analysis & interpretability, +5382,Interpretability and Analysis of Models for NLP,explanation faithfulness,model analysis & interpretability, +5383,Theme Track: Reality Check,(non-)generalizability,nlp engineering experiment, +5386,Interpretability and Analysis of Models for NLP,calibration/uncertainty,model analysis & interpretability, +5388,Machine Learning for NLP,representation learning,approaches for low-resource settings, +5397,Theme Track: Reality Check,(non-)generalizability,nlp engineering experiment|model analysis & interpretability, +5404,Speech and Multimodality,spoken language translation,nlp engineering experiment, +5425,NLP Applications,"healthcare applications, clincial nlp",data analysis, +5447,Resources and Evaluation,corpus creation|language resources,approaches for low-resource settings|data resources, +5453,Multilingualism and Cross-Lingual NLP,multilingualism|cross-lingual transfer|multilingual benchmarks|multilingual evaluation,model analysis & interpretability|publicly available software and pre-trained models|data resources|reproduction study,bengali|hindi|punjabi|tamil|gujarati|marathi +5464,Machine Learning for NLP,,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models|data resources|data analysis, +5482,Question Answering,reading comprehension,nlp engineering experiment, +5486,NLP Applications,"fact checking, rumour/misinformation detection",surveys, +5489,Dialogue and Interactive Systems,conversational modeling,publicly available software and pre-trained models, +5508,Machine Learning for NLP,model compression methods,"approaches for low-compute settings, efficiency", +5561,Machine Learning for NLP,few-shot learning,"approaches for low-compute settings, efficiency", +5563,Computational Social Science and Cultural Analytics,nlp tools for social analysis,publicly available software and pre-trained models|data resources|data analysis, +5564,Question Answering,math qa,nlp engineering experiment, +5565,Generation,text-to-text generation,nlp engineering experiment, +5566,"Syntax: Tagging, Chunking, and Parsing","chunking, shallow-parsing",nlp engineering experiment,chinese +5567,Question Answering,commonsense qa,nlp engineering experiment, +5568,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +5569,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",phrase/sentence embedding,nlp engineering experiment, +5570,Machine Learning for NLP,model compression methods,"approaches for low-compute settings, efficiency", +5571,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment,chinese +5572,Discourse and Pragmatics,coherence,nlp engineering experiment|model analysis & interpretability, +5573,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",applications,nlp engineering experiment|model analysis & interpretability, +5574,Information Retrieval and Text Mining,contrastive learning,nlp engineering experiment, +5575,Machine Learning for NLP,representation learning,nlp engineering experiment, +5576,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",argument generation,nlp engineering experiment|approaches for low-resource settings, +5577,Interpretability and Analysis of Models for NLP,probing,model analysis & interpretability, +5578,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",computational psycholinguistics,nlp engineering experiment, +5579,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",phrase/sentence embedding,nlp engineering experiment|model analysis & interpretability, +5580,NLP Applications,mathematical nlp,nlp engineering experiment|publicly available software and pre-trained models, +5581,Summarization,abstractive summarisation|factuality,nlp engineering experiment, +5582,Large Language Models,interpretability/analysis,model analysis & interpretability|publicly available software and pre-trained models, +5583,Ethics and NLP,model bias/unfairness mitigation,model analysis & interpretability, +5585,Dialogue and Interactive Systems,task-oriented,nlp engineering experiment, +5587,Information Extraction,zero/few-shot extraction,approaches for low-resource settings, +5589,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +5591,Question Answering,table qa,nlp engineering experiment, +5594,Information Extraction,named entity recognition and relation extraction,approaches for low-resource settings, +5596,Summarization,abstractive summarisation|multilingual summarisation,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings|publicly available software and pre-trained models, +5597,Machine Learning for NLP,generalization,nlp engineering experiment, +5599,Resources and Evaluation,corpus creation|benchmarking|nlp datasets|evaluation,model analysis & interpretability|data resources, +5600,Speech and Multimodality,spoken language translation,nlp engineering experiment|publicly available software and pre-trained models|data resources, +5601,Speech and Multimodality,spoken language translation,approaches for low-resource settings, +5603,Machine Learning for NLP,data augmentation,nlp engineering experiment|approaches for low-resource settings, +5605,Generation,domain adaptation,nlp engineering experiment, +5607,Multilingualism and Cross-Lingual NLP,multilingual pre-training,nlp engineering experiment, +5608,"Language Grounding to Vision, Robotics, and Beyond",cross-modal content generation,data analysis, +5609,Resources and Evaluation,evaluation,nlp engineering experiment, +5611,Machine Learning for NLP,multi-task learning|generalization|few-shot learning,nlp engineering experiment|approaches for low-resource settings, +5614,Generation,analysis|text-to-text generation,nlp engineering experiment|position papers, +5616,Large Language Models,applications,"nlp engineering experiment|approaches for low-compute settings, efficiency|approaches for low-resource settings", +5617,Interpretability and Analysis of Models for NLP,explanation faithfulness,nlp engineering experiment|model analysis & interpretability|theory, +5618,Machine Learning for NLP,parameter-efficient finetuning,"approaches for low-compute settings, efficiency|approaches for low-resource settings", +5619,Machine Learning for NLP,adversarial training,nlp engineering experiment, +5620,Resources and Evaluation,benchmarking,data resources, +5622,Dialogue and Interactive Systems,dialogue state tracking,nlp engineering experiment, +5623,Dialogue and Interactive Systems,evaluation and metrics|conversational modeling,nlp engineering experiment, +5624,Machine Learning for NLP,parameter-efficient finetuning,nlp engineering experiment, +5625,Machine Translation,automatic evaluation,"nlp engineering experiment|approaches for low-compute settings, efficiency|data resources", +5626,Ethics and NLP,ethical considerations in nlp applications,model analysis & interpretability, +5627,Interpretability and Analysis of Models for NLP,knowledge tracing/discovering/inducing,model analysis & interpretability, +5628,Interpretability and Analysis of Models for NLP,robustness,model analysis & interpretability, +5631,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",natural language inference,model analysis & interpretability|data resources, +5632,Resources and Evaluation,language resources|nlp datasets|evaluation,data resources,french +5633,NLP Applications,"healthcare applications, clincial nlp",nlp engineering experiment|approaches for low-resource settings, +5634,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training,nlp engineering experiment, +5635,Information Retrieval and Text Mining,passage retrieval|dense retrieval|contrastive learning,nlp engineering experiment, +5637,NLP Applications,"hate speech detection|healthcare applications, clincial nlp",nlp engineering experiment|model analysis & interpretability|data resources, +5638,Resources and Evaluation,evaluation methodologies,reproduction study|surveys, +5639,Dialogue and Interactive Systems,evaluation and metrics,publicly available software and pre-trained models, +5640,"Semantics: Sentence-level Semantics, Textual Inference, and Other Areas",reasoning,approaches for low-resource settings|data resources|position papers, +5642,Machine Learning for NLP,few-shot learning|human-in-the-loop / active learning,nlp engineering experiment, +5643,Question Answering,reading comprehension,nlp engineering experiment|model analysis & interpretability|data resources, +5644,Resources and Evaluation,reproducibility,position papers, +5646,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",linguistic theories,nlp engineering experiment, +5647,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment, +5648,Resources and Evaluation,corpus creation|language resources|nlp datasets|datasets for low resource languages,publicly available software and pre-trained models|data analysis, +5649,Dialogue and Interactive Systems,grounded dialog,"approaches for low-compute settings, efficiency", +5650,Summarization,conversational summarization,data resources, +5651,Resources and Evaluation,,data resources,german +5652,NLP Applications,code generation and understanding,nlp engineering experiment, +5653,Interpretability and Analysis of Models for NLP,robustness,model analysis & interpretability, +5654,Ethics and NLP,data ethics,nlp engineering experiment, +5655,Machine Translation,modelling,nlp engineering experiment, +5657,Machine Learning for NLP,few-shot learning|meta learning,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings, +5658,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",cognitive modeling,nlp engineering experiment, +5660,Interpretability and Analysis of Models for NLP,explanation faithfulness|hardness of samples,model analysis & interpretability, +5661,Resources and Evaluation,nlp datasets,data resources, +5662,Question Answering,reading comprehension,nlp engineering experiment, +5666,Interpretability and Analysis of Models for NLP,probing,model analysis & interpretability, +5667,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training,model analysis & interpretability, +5668,Speech and Multimodality,automatic speech recognition|spoken language understanding|qa via spoken queries,"nlp engineering experiment|approaches for low-compute settings, efficiency|approaches for low-resource settings", +5669,Information Extraction,event extraction|document-level extraction,nlp engineering experiment, +5670,Semantics: Lexical,,model analysis & interpretability, +5673,Computational Social Science and Cultural Analytics,nlp tools for social analysis,approaches for low-resource settings,french|dutch +5676,Large Language Models,scaling,"approaches for low-compute settings, efficiency|publicly available software and pre-trained models", +5677,NLP Applications,legal nlp,nlp engineering experiment, +5679,Resources and Evaluation,automatic creation and evaluation of language resources|nlp datasets,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models|data resources|data analysis, +5680,Generation,text-to-text generation,approaches for low-resource settings, +5682,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",linguistic theories|cognitive modeling|computational psycholinguistics,model analysis & interpretability|publicly available software and pre-trained models|data analysis|reproduction study,chinese +5683,Question Answering,knowledge base qa,data resources, +5684,Semantics: Lexical,polysemy|paraphrasing|metaphor|cognition,nlp engineering experiment|approaches for low-resource settings|data resources, +5685,Large Language Models,prompting|applications,nlp engineering experiment|approaches for low-resource settings, +5686,Multilingualism and Cross-Lingual NLP,multilingualism|cross-lingual transfer|mutlilingual representations,model analysis & interpretability, +5687,Interpretability and Analysis of Models for NLP,topic modeling,"nlp engineering experiment|approaches for low-compute settings, efficiency|approaches for low-resource settings|theory", +5688,Machine Learning for NLP,representation learning,model analysis & interpretability, +5691,Question Answering,generalization,surveys, +5692,Information Retrieval and Text Mining,dense retrieval,nlp engineering experiment, +5693,Large Language Models,pre-training|interpretability/analysis|continual learning|applications,nlp engineering experiment|model analysis & interpretability|publicly available software and pre-trained models, +5694,Information Extraction,named entity recognition and relation extraction|document-level extraction|zero/few-shot extraction,nlp engineering experiment, +5698,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",linguistic theories,nlp engineering experiment|model analysis & interpretability, +5699,Computational Social Science and Cultural Analytics,language/cultural bias analysis,model analysis & interpretability, +5701,Machine Learning for NLP,data augmentation,nlp engineering experiment|approaches for low-resource settings, +5703,Dialogue and Interactive Systems,conversational modeling,nlp engineering experiment, +5704,Question Answering,commonsense qa|semantic parsing|generalization,"nlp engineering experiment|approaches for low-compute settings, efficiency|data resources",chinese +5706,Machine Learning for NLP,meta learning,"nlp engineering experiment|approaches for low-compute settings, efficiency|approaches for low-resource settings", +5707,Information Extraction,event extraction,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings|surveys, +5708,Dialogue and Interactive Systems,human-in-the-loop|bias/toxicity|conversational modeling,nlp engineering experiment, +5713,NLP Applications,"fact checking, rumour/misinformation detection",nlp engineering experiment, +5714,Information Extraction,open information extraction,model analysis & interpretability|data analysis, +5721,Resources and Evaluation,nlp datasets,data resources,chinese +5722,Question Answering,commonsense qa,nlp engineering experiment, +5723,NLP Applications,security/privacy,nlp engineering experiment|model analysis & interpretability|reproduction study, +5724,Machine Learning for NLP,knowledge-augmented methods,"approaches for low-compute settings, efficiency", +5725,Speech and Multimodality,automatic speech recognition|spoken language understanding|multimodality,approaches for low-resource settings|publicly available software and pre-trained models, +5726,Resources and Evaluation,corpus creation|benchmarking|language resources|multilingual corpora|automatic creation and evaluation of language resources|nlp datasets|automatic evaluation of datasets|evaluation methodologies|evaluation|datasets for low resource languages|metrics,nlp engineering experiment|approaches for low-resource settings|publicly available software and pre-trained models|data resources,amharic|arabic|azerbaijani|bengali|burmese|chinese|french|gujarati|hausa|hindi|igbo|indonesian|japanese|kirundi|korean|kyrgyz|marathi|nepali|oromo|pashto|persian|pcm|portuguese|panjabi|russian|scottish gaelic|serbian|sinhala|somali|spanish|swahili|tamil|telugu|thai|tigrinya|turkish|ukrainian|urdu|uzbek|vietnamese|welsh|yoruba +5728,Semantics: Lexical,metaphor,nlp engineering experiment, +5729,Large Language Models,applications,approaches for low-resource settings,chinese +5730,Ethics and NLP,ethical considerations in nlp applications,"nlp engineering experiment|approaches for low-compute settings, efficiency", +5731,"Syntax: Tagging, Chunking, and Parsing","chunking, shallow-parsing",nlp engineering experiment|data resources, +5734,Machine Translation,few-shot/zero-shot mt|multilingual mt|vocabulary learning,nlp engineering experiment|model analysis & interpretability|approaches for low-resource settings, +5736,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training,model analysis & interpretability, +5738,"Language Grounding to Vision, Robotics, and Beyond",vision language navigation|cross-modal pretraining|cross-modal content generation|cross-modal application,nlp engineering experiment|publicly available software and pre-trained models|data resources, +5739,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",linguistic theories|cognitive modeling|computational psycholinguistics,model analysis & interpretability, +5740,Computational Social Science and Cultural Analytics,nlp tools for social analysis,model analysis & interpretability, +5741,Ethics and NLP,ethical considerations in nlp applications,nlp engineering experiment|model analysis & interpretability, +5742,Machine Learning for NLP,generalization,nlp engineering experiment, +5743,Question Answering,open-domain qa,nlp engineering experiment|model analysis & interpretability, +5744,Interpretability and Analysis of Models for NLP,robustness,model analysis & interpretability, +5745,Information Retrieval and Text Mining,dense retrieval,nlp engineering experiment, +5746,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +5748,Machine Learning for NLP,continual learning,nlp engineering experiment, +5749,Interpretability and Analysis of Models for NLP,free-text/natural language explanations,nlp engineering experiment|data resources|data analysis, +5750,Machine Learning for NLP,transfer learning / domain adaptation,nlp engineering experiment, +5751,Information Extraction,entity linking/disambiguation,nlp engineering experiment, +5755,Computational Social Science and Cultural Analytics,hate-speech detection,nlp engineering experiment|publicly available software and pre-trained models, +5756,Dialogue and Interactive Systems,grounded dialog,nlp engineering experiment|publicly available software and pre-trained models, +5758,"Language Grounding to Vision, Robotics, and Beyond",cross-modal application,nlp engineering experiment, +5760,NLP Applications,knowledge graphs,"nlp engineering experiment|approaches for low-compute settings, efficiency", +5761,Information Extraction,event extraction|multilingual extraction,model analysis & interpretability|approaches for low-resource settings, +5766,Computational Social Science and Cultural Analytics,human behavior analysis,data analysis, +5767,Information Extraction,document-level extraction,nlp engineering experiment|model analysis & interpretability|data resources, +5768,Dialogue and Interactive Systems,applications,nlp engineering experiment, +5769,Multilingualism and Cross-Lingual NLP,code-switching|mixed language,nlp engineering experiment, +5770,Computational Social Science and Cultural Analytics,nlp tools for social analysis,model analysis & interpretability, +5771,NLP Applications,knowledge graphs,nlp engineering experiment, +5772,Summarization,evaluation|factuality,nlp engineering experiment, +5775,Question Answering,commonsense qa,"nlp engineering experiment|approaches for low-compute settings, efficiency", +5777,Machine Learning for NLP,transfer learning / domain adaptation,model analysis & interpretability, +5784,"Language Grounding to Vision, Robotics, and Beyond",cross-modal application,nlp engineering experiment|model analysis & interpretability|reproduction study, +5785,NLP Applications,knowledge graphs,approaches for low-resource settings, +5786,Machine Learning for NLP,representation learning,nlp engineering experiment|theory, +5788,Ethics and NLP,model bias/unfairness mitigation,nlp engineering experiment|publicly available software and pre-trained models, +5790,Question Answering,open-domain qa,surveys, +5791,Information Extraction,open information extraction,nlp engineering experiment|data resources, +5792,Generation,efficient models,nlp engineering experiment, +5793,Interpretability and Analysis of Models for NLP,knowledge tracing/discovering/inducing,model analysis & interpretability, +5796,Machine Learning for NLP,representation learning,"model analysis & interpretability|approaches for low-compute settings, efficiency|approaches for low-resource settings", +5797,Theme Track: Reality Check,ai hype & expectations|lessons from other fields,data analysis|position papers|surveys, +5798,Multilingualism and Cross-Lingual NLP,multilingualism|cross-lingual transfer|mutlilingual representations|multilingual benchmarks|multilingual evaluation,approaches for low-resource settings|publicly available software and pre-trained models|data resources, +5802,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +5804,Speech and Multimodality,spoken language translation,nlp engineering experiment,hokkien +5805,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment|publicly available software and pre-trained models|data resources, +5806,Computational Social Science and Cultural Analytics,nlp tools for social analysis,nlp engineering experiment|data resources|data analysis, +5809,Information Extraction,named entity recognition and relation extraction|zero/few-shot extraction,approaches for low-resource settings, +5810,Machine Learning for NLP,knowledge-augmented methods|structured prediction,nlp engineering experiment, +5811,Machine Translation,automatic evaluation|interactive mt|modelling,nlp engineering experiment|data resources,korean +5812,Interpretability and Analysis of Models for NLP,probing|robustness,model analysis & interpretability, +5815,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment, +5816,Machine Learning for NLP,reinforcement learning,nlp engineering experiment|model analysis & interpretability, +5818,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment, +5822,Multilingualism and Cross-Lingual NLP,mutlilingual representations,model analysis & interpretability, +5824,Information Extraction,knowledge base construction,nlp engineering experiment|publicly available software and pre-trained models, +5826,"Linguistic Theories, Cognitive Modeling, and Psycholinguistics",cognitive modeling,model analysis & interpretability|publicly available software and pre-trained models|data resources|data analysis, +5828,Dialogue and Interactive Systems,conversational modeling,nlp engineering experiment, +5830,Linguistic Diversity,less-resourced languages|resources for less-resourced languages,approaches for low-resource settings|data resources, +5831,Dialogue and Interactive Systems,conversational modeling,"approaches for low-compute settings, efficiency", +5832,"Sentiment Analysis, Stylistic Analysis, and Argument Mining",argument mining|argument quality assessment|argument schemes and reasoning,nlp engineering experiment|approaches for low-resource settings, +5833,Question Answering,multihop qa|few-shot qa|open-domain qa,nlp engineering experiment|approaches for low-resource settings, +5834,Interpretability and Analysis of Models for NLP,probing,model analysis & interpretability, +5836,NLP Applications,"educational applications, gec, essay scoring",nlp engineering experiment|model analysis & interpretability|data analysis, +5837,Computational Social Science and Cultural Analytics,nlp tools for social analysis,nlp engineering experiment|publicly available software and pre-trained models|data analysis, +5840,Summarization,extractive summarisation|evaluation|factuality,nlp engineering experiment|data resources|data analysis, +5841,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training,nlp engineering experiment, +5842,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment, +5845,Question Answering,reading comprehension|reasoning|table qa,nlp engineering experiment|publicly available software and pre-trained models, +5846,NLP Applications,"healthcare applications, clincial nlp",nlp engineering experiment, +5847,Large Language Models,interpretability/analysis,model analysis & interpretability, +5848,Interpretability and Analysis of Models for NLP,adversarial attacks/examples/training|data shortcuts/artifacts|robustness,nlp engineering experiment, +5850,Resources and Evaluation,nlp datasets,data resources, +5854,Computational Social Science and Cultural Analytics,nlp tools for social analysis,data analysis, +5855,NLP Applications,mathematical nlp,nlp engineering experiment|data analysis,mathematica|latex|semantic latex +5856,Dialogue and Interactive Systems,task-oriented,nlp engineering experiment|publicly available software and pre-trained models|data resources, +5857,Machine Learning for NLP,optimization methods|meta learning,model analysis & interpretability|data analysis, +5858,Question Answering,multilingual qa|open-domain qa,nlp engineering experiment|approaches for low-resource settings|data resources, +5859,Large Language Models,pre-training|scaling,"approaches for low-compute settings, efficiency|publicly available software and pre-trained models", +5860,Interpretability and Analysis of Models for NLP,topic modeling,model analysis & interpretability, +5867,Dialogue and Interactive Systems,task-oriented|applications,nlp engineering experiment|approaches for low-resource settings, +5869,Information Extraction,named entity recognition and relation extraction,nlp engineering experiment|approaches for low-resource settings,