Document-level Relationship Extraction by Bidirectional Constraints of Beta Rules

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

Submission Type: Regular Long Paper

Submission Track: Information Extraction

Keywords: Document-level Relation Extraction, Logical Consistency, Beta Distribution, Bidirectional Constraints

TL;DR: We propose a rule-constrained framework that utilizes the learning of beta rules and logical bidirectional constraints to assist in document-level relationship extraction.

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 ($\sim$2.7 F1 score) and logical consistency($\sim$3.1 logic score). Furthermore, BCBR consistently outperforms two other logic constraint frameworks.

Submission Number: 2645