Source code for "A Double-Graph Based Framework for Frame Semantic Parsing" @ NAACL 2022
TL;DR: A framework for frame semantic parsing aims to utilize double-graph structure to inject knowledge into parsing and strengthen interacrions between arguments.
Paper Link: A Double-Graph Based Framework for Frame Semantic Parsing, TODO: this is early version of our paper, we will replace it with camera-ready version soon.
We find ontological frame knowledge can contribute to frame semantic parsing by both intra-frame and inter-frame reasoning. To make use of it, we build FKG (Frame Knowledge Graph) on definitions of FEs, frame relations and FE mappings. Besides, We regard frame semantic parsing as a process to add nodes in FSG (Frame Semantic Graph) incrementally, which can strengthen relations beween arguments and interactions of subtasks in frame semantic parsing.
pandas == 1.4.1
pytorch == 1.11.0
scipy == 1.8.0
stanza == 1.3.0
You can get some data files from scratch, we will also provide links to download remaining data files.
Please make sure your data folder structure as below.
.
├── dev_instance_dic.npy
├── exemplar_instance_dic.npy
├── fe_label_to_dict.npy
├── fn1.5
│ └── conll
│ ├── dev
│ ├── exemplar
│ ├── frames
│ ├── test
│ └── train
├── fndata-1.5
│ └── ...
├── frame-fe-dist-path
│ ├── fe_dis_matrix.npy
│ ├── fe_hash_idx.npy
│ ├── fe_path_matrix.npy
│ ├── frame_dis_matrix.npy
│ ├── frame_hash_idx.npy
│ └── frame_path_matrix.npy
├── glove.6B.200d.txt
├── graph
│ ├── frame_fe.npz
│ ├── frame_frame.npz
│ ├── inter_fe.npz
│ ├── intra_fe.npz
│ └── self_loop.npz
├── intra_frame_fe_relations.npy
├── parsed-v1.5
│ ├── FE.csv
│ ├── feRelations.csv
│ ├── frame.csv
│ ├── frameRelations.csv
│ ├── fulltext
│ ├── LU.csv
│ └── ...
├── test_instance_dic.npy
└── train_instance_dic.npy
TODO: replace them with one single shell data_preprocess.sh
Run data_preprocess.py to get data_instance_dic and word/lemma vectors from GloVe.
python data_preprocess.py
Then run dep_parsing.py to build dependency trees for each sentence. This will modify data_instance_dic produced by data_preprocess.py, and we DO NOT parse exemplar_instance_dic because it will take a lot time. You can modify dep_parsing.py to parse exemplar_insrtance_dic if you want to pretrain on exemplar sentences.
python dep_parsing.py
Run build_fe_name_dict.py to get fe_label_to_name.npy which stores a dict to map FE id to their name.
python build_fe_name_dict.py
Run parse_fe_xml.py and rel_graph_construct.py to build adj matrix for FKG.
python pretrain.py --save_model_path [prefix of your model path, e.g. ../model/pretrain_] --epoch 30
Fine-tuning on train instances:
python train.py --pretrain_model_path ../model/pretrain_30.bin --lr 6e-5 --save_model_path [path where you save fine-tuned model]
python train.py --save_model_path [path where you save trained model]
For more details of arguments, see config.py
python train.py --mode test --batch_size 2 --save_model_path [path where you save trained model]