Awesome

RE-DMP

The code for our ACL-2022 paper Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking

Please contact us at yhtian@uw.edu if you have any questions.

Citation

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

@inproceedings{tian-etal-2022-improving,
    title = "Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking",
    author = "Tian, Yuanhe and Song, Yan and Xia, Fei",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-acl.147",
    pages = "1875--1886",
}

Requirements

Our code works with the following environment.

• python=3.7
• pytorch=1.7

Downloading BERT and XLNet

In our paper, we use BERT and XLNet as the encoder. We follow the instructions to convert the TensorFlow checkpoints to the PyTorch version.

Note: for XLNet, it is possible that the resulting config.json misses the hyper-parameter n_token. You can manually add it and set its value to 32000 (which is identical to vocab_size).

Train and Test the model

Pre-training

Go to the pre-training folder for more information about model pre-training.

Fine-tuning

You can find the command lines to train and test models on a small sample data in run.sh.

Here are some important parameters:

• --do_train: train the model.
• --do_test: test the model.
• --use_bert: use BERT as encoder.
• --use_xlnet: use XLNet as encoder.
• --bert_model: the directory of pre-trained BERT/XLNet model.
• --model_name: the name of model to save.