Feature-Level Debiased Natural Language Understanding (AAAI 2023)

This repository is the implementation of our AAAI 2023 Paper Feature-Level Debiased Natural Language Understanding. Please contact Yougang Lyu (youganglyu@gmail.com) if you have any question.

Datasets and Checkpoints

Download the processed dataset and checkpoints from the Google Drive. The downloaded datasets should be moved into /PATH_TO_DATA_DIR.

The downloaded ckpt files should be moved into /PATH_TO_OUTPUT_DIR.

Quick Start

To train the DCT model, run:

sh scripts/mnli_dct_train.sh #bert_path
sh scripts/fever_dct_train.sh #bert_path
sh scripts/snli_dct_train.sh #bert_path

You can also test the model has been saved by us.

sh scripts/mnli_dct_eval.sh #checkpoint_path
sh scripts/fever_dct_eval.sh #checkpoint_path
sh scripts/snli_dct_eval.sh #checkpoint_path

Bias Extractability

The code for evaluating the extractability of biased features in the model representation is https://github.com/technion-cs-nlp/bias-probing.

Citation

If you find our work useful, please cite our paper as follows:

@inproceedings{DBLP:conf/aaai/LyuLYRRZYR23,
  author       = {Yougang Lyu and
                  Piji Li and
                  Yechang Yang and
                  Maarten de Rijke and
                  Pengjie Ren and
                  Yukun Zhao and
                  Dawei Yin and
                  Zhaochun Ren},
  title        = {Feature-Level Debiased Natural Language Understanding},
  booktitle    = {Proceedings of {AAAI}},
  pages        = {13353--13361},
  year         = {2023},
}