This repository contains the source code and dataset for the paper: PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction. Hengyi Zheng, Rui Wen, Xi Chen et al. ACL 2021.
The main requirements are:
- python==3.7.9
- pytorch==1.6.0
- transformers==3.2.0
- tqdm
Or you can just download our preprocessed datasets.
1. Get pre-trained BERT model for PyTorch
Download BERT-Base-Cased which contains pytroch_model.bin, vocab.txt and config.json. Put these under ./pretrain_models.
2. Build Data
Put our preprocessed datasets under ./data.
3. Train
Just run the script in ./script by sh train.sh.
For example, to train the model for NYT* dataset, update the train.sh as:
python ../train.py \
--ex_index=1 \
--epoch_num=100 \
--device_id=0 \
--corpus_type=NYT-star \
--ensure_corres \
--ensure_rel
4. Evaluate
Just run the script in ./script by sh evaluate.sh.
For example, to train the model for NYT* dataset, update the evaluate.sh as:
python ../evaluate.py \
--ex_index=1 \
--device_id=0 \
--mode=test \
--corpus_type=NYT-star \
--ensure_corres \
--ensure_rel \
--corres_threshold=0.5 \
--rel_threshold=0.1