- Tiecheng Li, Jiarui Tang, Lei Li. 2022. Relational Reasoning Model Based on Evidence Sentences for Document-level Relation Extraction. Abstract: Document-level relationship extraction (DRE) aims to extract relationships between entity pairs in a document. Different from sentence-level relation extraction (SRE), there may be cross-sentence relationships with multiple sentences around two entities in DRE. In most cases, only some of the sentences involve the relationship information, we call them evidence sentences, which are very important when the context of entity pairs is complicated and multi-hop relational reasoning is required. Although the current works related to DRE have achieved good results, they mostly ignore the information of evidence sentences. They usually use only the sentences containing entities, which leads to errors because of noisy sentences. To solve this problem, this paper proposes the Evidence sentence detection and Relational reasoning Neural Network (ERNN) model. ERNN is a multi-task joint model that can detect evidence sentences and extract relationships based on evidence sentences. ERNN first uses the evidence sentences to construct a reasoning path, and then classifies the relationship. The experiments on the DocRED dataset shows that ERNN performs better than the existing models in extracting relationships that require complex inference processes.
