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This repo contains the code and data for the EMNLP 2022 findings paper TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages which can be found here.
The TyDiP dataset is licensed under the CC BY 4.0 license.
The data
folder contains the different files we release as part of the TyDiP dataset. The TyDiP dataset comprises of an English train set and English test set that are adapted from the Stanford Politeness Corpus, and test data in 9 more languages (Hindi, Korean, Spanish, Tamil, French, Vietnamese, Russian, Afrikaans, Hungarian) that we annotated.
data/
├── all
├── binary
└── unlabelled_train_sets
data/all
consists of the complete train and test sets.
data/binary
is a filtered version of the above where sentences from the top and bottom 25 percentile of scores is only present. This is the data that we used for training and evaluation in the paper.
data/unlabelled_train_sets
politeness_regresor.py
is used for training and evaluation of transformer models
To train a model
python politeness_regressor.py --train_file data/binary/en_train_binary.csv --test_file data/binary/en_test_binary.csv --model_save_location model.pt --pretrained_model xlm-roberta-large --gpus 1 --batch_size 4 --accumulate_grad_batches 8 --max_epochs 5 --checkpoint_callback False --logger False --precision 16 --train --test --binary --learning_rate 5e-6
To test this trained model on $lang
python politeness_regressor.py --test_file data/binary/${lang}_test_binary.csv --load_model model.pt --gpus 1 --batch_size 32 --test --binary
XLM-Roberta Large finetuned on the English train set (as discussed and evaluated in the paper) can be found here
strategies
contains the processed strategy lexicon for different languages. strategies/learnt_strategies.xlsx
contains the human edited strategies for 4 langauges
annotation.html
contains the UI used for conducting data annotation
If you use the English train or test data, please cite the Stanford Politeness Dataset
@inproceedings{danescu-niculescu-mizil-etal-2013-computational,
title = "A computational approach to politeness with application to social factors",
author = "Danescu-Niculescu-Mizil, Cristian and
Sudhof, Moritz and
Jurafsky, Dan and
Leskovec, Jure and
Potts, Christopher",
booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P13-1025",
pages = "250--259",
}
If you use the test data from the 9 target languages, please cite our paper
@inproceedings{srinivasan-choi-2022-tydip,
title = "{T}y{D}i{P}: A Dataset for Politeness Classification in Nine Typologically Diverse Languages",
author = "Srinivasan, Anirudh and
Choi, Eunsol",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.420",
pages = "5723--5738",
}