Evidence and Axial Attention Guided Document-level Relation Extraction

Abstract

Document-level Relation Extraction (DocRE) aims to identify semantic relations among multiple entity pairs within a document. Most of the previous DocRE methods take the entire document as input. However, for human annotators, a small subset of sentences in the document, namely the evidence, is sufficient to infer the relation of an entity pair. Additionally, a document usually contains multiple entities, and these entities are scattered throughout various location of the document. Previous models use these entities independently, ignore the global interdependency among relation triples. To handle above issues, we propose a novel framework EAAGRE (Evidence and Axial Attention Guided Relation Extraction). Firstly, we use human-annotated evidence labels to supervise the attention module of DocRE system, making the model pay attention to the evidence sentences rather than others. Secondly, we construct an entity-level relation matrix and use axial attention to capture the global interactions among entity pairs. By doing so, we further extract the relations that require multiple entity pairs for prediction. We conduct various experiments on DocRED and have some improvement compared to baseline models, verifying the effectiveness of our model.