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Document-level relation extraction (RE) aims to identify the relationsbetween entities throughout an entire document. It needs complex reasoningskills to synthesize various knowledge such as coreferences and commonsense.Large-scale knowledge graphs (KGs) contain a wealth of real-world facts, andcan provide valuable knowledge to document-level RE. In this paper, we proposean entity knowledge injection framework to enhance current document-level REmodels. Specifically, we introduce coreference distillation to injectcoreference knowledge, endowing an RE model with the more general capability ofcoreference reasoning. We also employ representation reconciliation to injectfactual knowledge and aggregate KG representations and document representationsinto a unified space. The experiments on two benchmark datasets validate thegeneralization of our entity knowledge injection framework and the consistentimprovement to several document-level RE models.

Enhancing Document-level Relation Extraction by Entity Knowledge Injection