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Document-level Relation Extraction (DRE) aims to recognize the relationsbetween two entities. The entity may correspond to multiple mentions that spanbeyond sentence boundary. Few previous studies have investigated the mentionintegration, which may be problematic because coreferential mentions do notequally contribute to a specific relation. Moreover, prior efforts mainly focuson reasoning at entity-level rather than capturing the global interactionsbetween entity pairs. In this paper, we propose two novel techniques, ContextGuided Mention Integration and Inter-pair Reasoning (CGM2IR), to improve theDRE. Instead of simply applying average pooling, the contexts are utilized toguide the integration of coreferential mentions in a weighted sum manner.Additionally, inter-pair reasoning executes an iterative algorithm on theentity pair graph, so as to model the interdependency of relations. We evaluateour CGM2IR model on three widely used benchmark datasets, namely DocRED, CDR,and GDA. Experimental results show that our model outperforms previousstate-of-the-art models.

Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning