Entity and Evidence Guided Relation Extraction for DocRED

• URL: http://arxiv.org/abs/2008.12283v1
• Date: Thu, 27 Aug 2020 17:41:23 GMT
• Title: Entity and Evidence Guided Relation Extraction for DocRED
• Authors: Kevin Huang, Guangtao Wang, Tengyu Ma and Jing Huang
• Abstract summary: We pro-pose a joint training frameworkE2GRE(Entity and Evidence Guided Relation Extraction)for this task. We introduce entity-guided sequences as inputs to a pre-trained language model (e.g. BERT, RoBERTa) These entity-guided sequences help a pre-trained language model (LM) to focus on areas of the document related to the entity. We evaluate our E2GRE approach on DocRED, a recently released large-scale dataset for relation extraction.
• Score: 33.69481141963074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Document-level relation extraction is a challenging task which requires reasoning over multiple sentences in order to predict relations in a document. In this paper, we pro-pose a joint training frameworkE2GRE(Entity and Evidence Guided Relation Extraction)for this task. First, we introduce entity-guided sequences as inputs to a pre-trained language model (e.g. BERT, RoBERTa). These entity-guided sequences help a pre-trained language model (LM) to focus on areas of the document related to the entity. Secondly, we guide the fine-tuning of the pre-trained language model by using its internal attention probabilities as additional features for evidence prediction.Our new approach encourages the pre-trained language model to focus on the entities and supporting/evidence sentences. We evaluate our E2GRE approach on DocRED, a recently released large-scale dataset for relation extraction. Our approach is able to achieve state-of-the-art results on the public leaderboard across all metrics, showing that our E2GRE is both effective and synergistic on relation extraction and evidence prediction.

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