RE-AGCN

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

You can e-mail Yuanhe Tian at yhtian@uw.edu, if you have any questions.

Citation

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

@inproceedings{tian-etal-2021-dependency,
    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 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    pages = "4458--4471",
}

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.

HenryCai11/RE-AGCN