trellixvulnteam / re-agcn_ujwr

RE-AGCN

This is the implementation of Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks at ACL 2021.

You can e-mail Guimin Chen at [email protected] or [email protected] or Yuanhe Tian at [email protected], if you have any questions.

Citation

If you use or extend our work, please cite our paper at ACL 2021.

@inproceedings{tian2021dependency,
  title={Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks},
  author={Tian, Yuanhe and Chen, Guimin and Song, Yan and Wan, Xiang},
  booktitle={Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing},
  year={2021}
}

Requirements

Our code works with the following environment.

• python>=3.7
• pytorch>=1.3

Dataset

To obtain the data, you can go to data directory for details.

Downloading BERT

In our paper, we use BERT (paper) as the encoder.

For BERT, please download pre-trained BERT-Base and BERT-Large English from Google or from HuggingFace. If you download it from Google, you need to convert the model from TensorFlow version to PyTorch version.

Downloading our pre-trained RE-AGCN

For RE-AGCN, you can download the models we trained in our experiments from Google Drive.

Run on Sample Data

Run run_sample.sh to train a model on the small sample data under the sample_data directory.

Training and Testing

You can find the command lines to train and test models in run_train.sh and run_test.sh, respectively.

Here are some important parameters:

• --do_train: train the model.
• --do_eval: test the model.

To-do List

• Regular maintenance.

You can leave comments in the Issues section, if you want us to implement any functions.