DocRE with Reconstruction

This repository is the PyTorch implementation of our DocRE model with reconstruction in AAAI 2021 Paper "Document-Level Relation Extraction with Reconstruction".

Requirements and Installation

python3>=3.6
pytorch>=1.5
scikit-learn>=0.21.2
wandb>=0.95

Dataset

Download metadata from TsinghuaCloud or GoogleDrive for baseline method and put them into prepro_data folder.

For the dataset and pretrained embeddings, please download it here, which are officially provided by DocRED: A Large-Scale Document-Level Relation Extraction Dataset and put them into data folder.

Proprocessing

python3 gen_data.py --in_path data --out_path prepro_data
python3 gen_graph_data.py --in_path prepro_data --worker_num 24

Training

CUDA_VISIBLE_DEVICES=0 python3 train.py --model_name DynGraph --save_name checkpoint_DynGraph --train_prefix dev_train --test_prefix dev_dev

Testing

CUDA_VISIBLE_DEVICES=0 python3 train.py --model_name DynGraph --save_name checkpoint_DynGraph --train_prefix dev_train --test_prefix dev_dev --eval_model True --rel_theta 0.3601

Citation

If you find our work or the code useful, please consider cite our paper using:

@inproceedings{docred-rec,
 author = {Wang Xu and Kehai Chen and Tiejun Zhao},
 booktitle = {The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)},
 title = {Document-Level Relation Extraction with Reconstruction},
 year = {2021}
}