COREX: Document-level Relation Extraction Framework with Consistent Two-Hop Reasoning and Evidence Sentence Prediction

Document-level relation extraction (DocRE) focuses on identifying relationships between entities within a document. It differs from sentence-level extraction as it involves understanding relationships over longer distances, including across sentences or paragraphs. A major challenge in DocRE is to focus on crucial evidence and bridge entities, defined as entities that link others in relationships spanning multiple sentences. Therefore, we propose a novel, efficient representation to support two-hop reasoning. Supervised by evidence prediction and relation extraction, this explicit multi-hop reasoning representation notably improves long-distance reasoning capabilities in DocRE. To address the issue of class imbalance in DocRE, we revise the Adaptive Focal Loss (AFL) and incorporate it into our framework. Moreover, we develop a Weighted Fusion Layer, designed to optimize the utilization of evidence, thereby ensuring a comprehensive yet focused analytical perspective. Our framework, named COREX, has shown superior performance in extensive experiments on three benchmark datasets. Its performance exceeds the baseline by 1.8/1.58 in Ign F1/Inter F1 score on the DocRED leaderboard.