RE-TaMM

This is the implementation of Relation Extraction with Type-aware Map Memories of Word Dependencies at ACL 2021.

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

Visit our homepage to find more our recent research and softwares for NLP (e.g., pre-trained LM, POS tagging, NER, sentiment analysis, relation extraction, datasets, etc.).

Upgrades of RE-TaMM

We are improving our RE-TaMM. For updates, please visit HERE.

Citation

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

@article{chen2021relation,
  title={Relation Extraction with Type-aware Map Memories of Word Dependencies},
  author={Chen, Guimin and Tian, Yuanhe and Song, Yan and Wan, Xiang},
  journal={Findings of the Association for Computational Linguistics: ACLIJCNLP},
  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 and RE-TaMM

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.

For RE-TAMM, 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.