Awesome
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.