Denoising Relation Extraction from Document-level Distant Supervision
Chaojun Xiao, Yuan Yao, Ruobing Xie, Xu Han, Zhiyuan Liu, Maosong Sun, Fen Lin, Leyu Lin
Abstract
Distant supervision (DS) has been widely adopted to generate auto-labeled data for sentence-level relation extraction (RE) and achieved great results. However, the existing success of DS cannot be directly transferred to more challenging document-level relation extraction (DocRE), as the inevitable noise caused by DS may be even multiplied in documents and significantly harm the performance of RE. To alleviate this issue, we propose a novel pre-trained model for DocRE, which de-emphasize noisy DS data via multiple pre-training tasks. The experimental results on the large-scale DocRE benchmark show that our model can capture useful information from noisy data and achieve promising results.- Anthology ID:
- 2020.emnlp-main.300
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3683–3688
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.300
- DOI:
- 10.18653/v1/2020.emnlp-main.300
- Cite (ACL):
- Chaojun Xiao, Yuan Yao, Ruobing Xie, Xu Han, Zhiyuan Liu, Maosong Sun, Fen Lin, and Leyu Lin. 2020. Denoising Relation Extraction from Document-level Distant Supervision. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3683–3688, Online. Association for Computational Linguistics.
- Cite (Informal):
- Denoising Relation Extraction from Document-level Distant Supervision (Xiao et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2020.emnlp-main.300.pdf
- Code
- thunlp/DSDocRE
- Data
- DocRED