This repository is the official implementation of DocuNet, which is model proposed in a paper: Document-level Relation Extraction as Semantic Segmentation, accepted by IJCAI2021 main conference.
- ❗NOTE: Docunet is integrated in the knowledge extraction toolkit DeepKE.
This paper innovatively proposes the DocuNet model, which first regards the document-level relation extraction as the semantic segmentation task in computer vision.
To install requirements:
pip install -r requirements.txt
To train the DocuNet model in the paper on the dataset DocRED, run this command:
>> bash scripts/run_docred.sh # use BERT/RoBERTa by setting --transformer-typeTo train the DocuNet model in the paper on the dataset CDR and GDA, run this command:
>> bash scripts/run_cdr.sh # for CDR
>> bash scripts/run_gda.sh # for GDATo evaluate the trained model in the paper, you setting the --load_path argument in training scripts. The program will log the result of evaluation automatically. And for DocRED it will generate a test file result.json in the official evaluation format. You can compress and submit it to Colab for the official test score.
Our model achieves the following performance on :
Document-level Relation Extraction on DocRED
| Model | Ign F1 on Dev | F1 on Dev | Ign F1 on Test | F1 on Test |
|---|---|---|---|---|
| DocuNet-BERT (base) | 59.86±0.13 | 61.83±0.19 | 59.93 | 61.86 |
| DocuNet-RoBERTa (large) | 62.23±0.12 | 64.12±0.14 | 62.39 | 64.55 |
Document-level Relation Extraction on CDR and GDA
| Model | CDR | GDA |
|---|---|---|
| DocuNet-SciBERT (base) | 76.3±0.40 | 85.3±0.50 |
Part of our code is borrowed from https://github.com/wzhouad/ATLOP, many thanks. You can refer to https://github.com/fenchri/edge-oriented-graph for the detailed preprocessing process of GDA and CDR datasets (acquire the file of train_filter.data, dev_filter.data and test_filter.data).
If you use or extend our work, please cite the paper as follows:
@inproceedings{ijcai2021-551,
title = {Document-level Relation Extraction as Semantic Segmentation},
author = {Zhang, Ningyu and Chen, Xiang and Xie, Xin and Deng, Shumin and Tan, Chuanqi and Chen, Mosha and Huang, Fei and Si, Luo and Chen, Huajun},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Zhi-Hua Zhou},
pages = {3999--4006},
year = {2021},
month = {8},
note = {Main Track}
doi = {10.24963/ijcai.2021/551},
url = {https://doi.org/10.24963/ijcai.2021/551},
}