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Compared with traditional sentence-level relation extraction, document-levelrelation extraction is a more challenging task where an entity in a documentmay be mentioned multiple times and associated with multiple relations.However, most methods of document-level relation extraction do not distinguishbetween mention-level features and entity-level features, and just apply simplepooling operation for aggregating mention-level features into entity-levelfeatures. As a result, the distinct semantics between the different mentions ofan entity are overlooked. To address this problem, we propose RSMAN in thispaper which performs selective attentions over different entity mentions withrespect to candidate relations. In this manner, the flexible andrelation-specific representations of entities are obtained which indeed benefitrelation classification. Our extensive experiments upon two benchmark datasetsshow that our RSMAN can bring significant improvements for some backbone modelsto achieve state-of-the-art performance, especially when an entity havemultiple mentions in the document.

Relation-Specific Attentions over Entity Mentions for Enhanced Document-Level Relation Extraction