EIDER: Evidence-enhanced Document-level Relation Extraction

Anonymous

Abstract: Document-level relation extraction (DocRE) aims at extracting the semantic relations among entity pairs in a document. In DocRE, a subset of the sentences in a document, called the evidence sentences, might be sufficient for predicting the relation between a specific entity pair. To make better use of the evidence sentences, in this paper, we propose a three-stage evidence-enhanced DocRE framework called Eider consisting of joint relation and evidence extraction, evidence-centered relation extraction (RE), and fusion of extraction results. We first jointly train an RE model with a simple and memory-efficient evidence extraction model. Then, we construct pseudo documents based on the extracted evidence sentences and run the RE model again. Finally, we fuse the extraction results of the first two stages using a blending layer and make a final prediction. Extensive experiments show that our proposed framework achieves state-of-the-art performance on the DocRED dataset, outperforming the second-best method by 1.37/1.26 Ign F1/F1. In particular, Eider-RoBERTa$_\text{large}$ significantly improves the performance on entity pairs requiring co-reference and multi-hop reasoning by 1.98/2.08 F1, respectively, which cover around 75\% of the cross-sentence samples.