Dual Reasoning Based Pairwise Representation Network for Document Level Relation Extraction

Relation extraction is the task of extracting relational facts between entities from plain text. When the extraction scope is extended to the document level, entities may exist in dif-ferent sentences. This requires the model to consider the in-teraction between multiple sentences comprehensively. Thus, document-level relation extraction becomes especially chal-lenging. Most existing models adopt entity representation learning to tackle this challenge. Nevertheless, they gener-ally perform representations of individual entities rather than directly modeling the dependencies between entities reflect target relation, which may cause wrong relation reasoning. To solve this issue, we propose a novel dual reasoning-based pairwise representation network. Specifically, we perform the semantic and syntactic-based reasoning to model the associ-ation between entities, and towards the capture of relational information in the document through an unique contextual se-lection for each entity pair. Experiments on three benchmark datasets demonstrate the inspiring performance improvement over state-of-the-art relation extraction models.