Source code for "Denoising Relation Extraction from Document-level Distant Supervision".
- Python3
- Pytorch>=1.5.0
- apex>=0.1
- numpy
- transformers>=2.5.1
Data can be downloaded from Google Drive.
Please put the data file in the data/.
Run the following command to train the pre-denoising model:
python3 train.py -c config/PreDenoising.config -g 0
After training the pre-denoising model, run the following commands to generate rank score for all data:
mkdir data/rank_result
# Generate the rank_result for test data.
python3 test.py -g 0 \
--config config/PreDenoising.config \
--test_file data/test.json \
--checkpoint checkpoint/PreDenoise/3.pkl \
--result_score data/rank_result/test_score.npy \
--result_title data/rank_result/test_title.json
# Generate the rank_result for valid data.
python3 test.py -g 0 \
--config config/PreDenoising.config \
--test_file data/dev.json \
--checkpoint checkpoint/PreDenoise/3.pkl \
--result_score data/rank_result/valid_score.npy \
--result_title data/rank_result/valid_title.json
# Generate the rank_result for train_annotated data.
python3 test.py -g 0 \
--config config/PreDenoising.config \
--test_file data/train_annotated.json \
--checkpoint checkpoint/PreDenoise/3.pkl \
--result_score data/rank_result/train_annotated_score.npy \
--result_title data/rank_result/train_annotated_title.json
# Generate the rank_result for train_distant data.
python3 test.py -g 0 \
--config config/PreDenoising.config \
--test_file data/train_distant.json \
--checkpoint checkpoint/PreDenoise/3.pkl \
--result_score data/rank_result/train_distant_score.npy \
--result_title data/rank_result/train_distant_title.json
python3 train.py -c config/PreTraining.config -g 0
# Choose an appropriate model to fine-tune
python3 train.py -c config/FineTuning.config -g 0 \
--pretrained_bert_path checkpoint/PreTrain/epoch_15/
If you use the code, please cite this paper:
@inproceedings{xiao2020denoise,
title={Denoising Relation Extraction from Document-level Distant Supervision},
author={Xiao, Chaojun and Yao, Yuan and Xie, Ruobing and Han, Xu and Liu, Zhiyuan and Sun, Maosong and Lin, Fen and Lin, Leyu},
booktitle={Proceedings of EMNLP},
year={2020}
}