Document-level Relationship Extraction by Bidirectional Constraints of Beta Rules (2023)

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.

## introduction in recent years, docre attracts significant attention from researchers, with its intention to distinguish the relations between entity pairs in the documents. it's not limited to sentence-level relation extraction @xcite @xcite . it aims to uncover the dependencies between entities in different sentences of one document @xcite . the challenges in docre mainly include two aspects: first, it's difficult to capture complex long-range dependencies between entity pairs in documents; second, it's prone to errors in logical reasoning due to lack of logic. to address the aforementioned challenges, the academic community has made a lot of efforts. based on the use of rules, we can classify previous docre works into two categories: plain docre models and logic constraint docre models. in plain docre models, attention is mainly given to learning more powerful implicit representations. these methods include models based on sequence encoders @xcite @xcite , and models based on graph encoders @xcite . although these methods have achieved decent results, their inferences are non-transparent and lack logic, making them prone to errors in logical inference. meanwhile, the combination of relation extraction frameworks and logical rules has alleviated the issues of low transparency and weak logic. as the figure 1 shows, we can derive two relations based_in0 (p orsche, argentina) and gpe0 (argentina, argentine) from the document. when the rule is applied,we can predict the relation based_in0-x between p orsche and argentina. logire @xcite is the first work that introduces logical rules into document-level relation extraction. it employed the expectation-maximization (em) algorithm to iteratively update the rule generator and relation extractor, optimizing the results of relation extraction. however, rule generator and relation extractor are in isolation. the em algorithm enables joint optimization of the two models, but it can still lead to suboptimal results. milr @xcite addresses the issue of suboptimality by jointly training the relation classification and rule consistency losses. but milr utilizes confidence-based methods to mine rules, which can lead to pseudo rules with high standard-confidence but low support, affecting the effectiveness of relation extraction. in the paper, we propose bcbr, a novel framework which assists relation extraction with the help of logical rules. bcbr models the bidirectional constraints of beta rules and optimizes relation extraction through rule consistency loss. (1) the prior rule set derived from the documents is different from the one extracted from the knowledge graph. textual data has a relatively small volume, and the inter-document correlations are low, leading to sparsity in the prior rule set. thus, general rule miners in previous works cause the prevalence of pseudo rules with high standard-confidence but low support. to tackle this problem, we utilize the beta distribution to model the rules and consider both their successful predictions and failures to filter the rules. (2) in addition, we discovered that the constraints between rule head and rule body are bidirectional, while previous methods often only considered the forward constraints from rule body to rule head. therefore, we introduce reverse constraints from rule head to rule body. (3) finally, based on above constraints, we reconstruct the rule consistency loss to enhance the performance of the original docre models. we summarize our contributions as follows: ## method in this chapter, we introduce our framework -bidirectional constraints of beta rules(bcbr) (fig. 2 ). we define concepts related to docre and rules(sec.3.1). then we propose a novel rule extraction method(sec.3.2) and model bidirectional logic constraints based on rules(sec.3.3). finally, we construct rule consistency loss and jointly train with relation classification loss to enhance relation extraction performance(sec.3.4). on the basis of closed world assumption(cwa) @xcite , we introduce two concepts: standard confidence and head coverage. standard confidence refers to the conditional probability of rule head being satisfied given that the rule body is satisfied. the standard confidence of rule s can be modeled as the following conditional probability distribution: