Eider

Code for ACL 2022 Finding paper "EIDER: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion"

Dataset

The DocRED dataset can be downloaded following the instructions at link.

The expected structure of files is:

Eider
 |-- dataset
 |    |-- docred
 |    |    |-- train_annotated.json        
 |    |    |-- train_distant.json
 |    |    |-- dev.json
 |    |    |-- test.json
 |-- meta
 |    |-- rel2id.json

Coreference Results Required by Eider_rule

We use hoi as the coreference model for Eider_rule. The processed data can be found here.

The expected structure of files is:

Eider
 |-- coref_results
 |    |-- train_annotated_coref_results.json
 |    |-- dev_coref_results.json
 |    |-- test_coref_results.json

Training and Inference

Train Eider-BERT on DocRED with the following commands:

>> bash scripts/train_bert.sh eider test hoi
>> bash scripts/test_bert.sh eider test hoi

Alternatively, you can train Eider-RoBERTa using:

>> bash scripts/train_roberta.sh eider test hoi
>> bash scripts/test_roberta.sh eider test hoi

The commands for Eider_rule is similar:

>> bash scripts/train_bert.sh eider test hoi # BERT
>> bash scripts/test_bert.sh eider test hoi

>> bash scripts/train_roberta.sh eider test hoi # RoBERTa
>> bash scripts/test_roberta.sh eider test hoi

Citation

If you make use of this code in your work, please kindly cite the following paper:

@inproceedings{xie2021eider,
      title={EIDER: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion}, 
      author={Yiqing Xie and Jiaming Shen and Sha Li and Yuning Mao and Jiawei Han},
      year={2022},
      booktitle = {Findings of the 60th Annual Meeting of the Association for Computational Linguistics},
      publisher = {Association for Computational Linguistics},
}

xblackleopard/Eider

Eider

Dataset

Coreference Results Required by Eider_rule

Training and Inference

Citation