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Document-level relation extraction is a challenging task which requiresreasoning over multiple sentences in order to predict relations in a document.In this paper, we pro-pose a joint training frameworkE2GRE(Entity and EvidenceGuided Relation Extraction)for this task. First, we introduce entity-guidedsequences as inputs to a pre-trained language model (e.g. BERT, RoBERTa). Theseentity-guided sequences help a pre-trained language model (LM) to focus onareas of the document related to the entity. Secondly, we guide the fine-tuningof the pre-trained language model by using its internal attention probabilitiesas additional features for evidence prediction.Our new approach encourages thepre-trained language model to focus on the entities and supporting/evidencesentences. We evaluate our E2GRE approach on DocRED, a recently releasedlarge-scale dataset for relation extraction. Our approach is able to achievestate-of-the-art results on the public leaderboard across all metrics, showingthat our E2GRE is both effective and synergistic on relation extraction andevidence prediction.

Entity and Evidence Guided Relation Extraction for DocRED