ET-MIER: Entity Type-guided Key Mention Identification and Evidence Retrieval for Document-level Relation Extraction

Document-level relation extraction (DocRE)task aims to identify relations between entities in a document.In DocRE, an entity may appear in multiple sentences of a document in the form of mentions.In addition, relation inference requires the use of evidence sentences that can provide key clues to entity pairs.These make DocRE more challenging than sentencelevel relation extraction.Existing work does not fully distinguish the contribution of different mentions to entity representation and the importance of mentions in evidence sentences.To address these issues, we observe that entity types can provide consistent semantic constraints for entities of the same type and implicitly preclude impossible relations between entities, which may help the model better understand both intra-and inter-entity mentions.Therefore, we propose a novel model ET-MIER, which for the first time leverages Entity Types to guide key Mention Identification and Evidence Retrieval.In this way, entity types not only help learn better entity representation but also enhance evidence retrieval, both of which are crucial for DocRE.We conduct experiments on widely-adopted datasets and show that our model achieves state-of-the-art performance.1 Original Document: [0] The Second River, or Watsessing River, in the state of New Jersey in the United States , … [6] Finally, it joins the Passaic River, on its way to Newark Bay.[8] The Reformed Dutch Church of Second River is named after this river .… Pre-trained Encoder