Document-level Relationship Extraction by Bidirectional Constraints of Beta Rules

Yichun Liu, Zizhong Zhu, Xiaowang Zhang, Zhiyong Feng, Daoqi Chen, Yaxin Li

Abstract
Document-level Relation Extraction (DocRE) aims to extract relations among entity pairs in documents. Some works introduce logic constraints into DocRE, addressing the issues of opacity and weak logic in original DocRE models. However, they only focus on forward logic constraints and the rules mined in these works often suffer from pseudo rules with high standard-confidence but low support. In this paper, we proposes Bidirectional Constraints of Beta Rules(BCBR), a novel logic constraint framework. BCBR first introduces a new rule miner which model rules by beta contribtion. Then forward and reverse logic constraints are constructed based on beta rules. Finally, BCBR reconstruct rule consistency loss by bidirectional constraints to regulate the output of the DocRE model. Experiments show that BCBR outperforms original DocRE models in terms of relation extraction performance (~2.7 F1 score) and logical consistency(~3.1 logic score). Furthermore, BCBR consistently outperforms two other logic constraint frameworks.
Anthology ID:
2023.emnlp-main.138
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2256–2266
Language:
URL:
https://aclanthology.org/2023.emnlp-main.138
DOI:
10.18653/v1/2023.emnlp-main.138
Bibkey:
Cite (ACL):
Yichun Liu, Zizhong Zhu, Xiaowang Zhang, Zhiyong Feng, Daoqi Chen, and Yaxin Li. 2023. Document-level Relationship Extraction by Bidirectional Constraints of Beta Rules. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2256–2266, Singapore. Association for Computational Linguistics.
Cite (Informal):
Document-level Relationship Extraction by Bidirectional Constraints of Beta Rules (Liu et al., EMNLP 2023)
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