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Article overview
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Eider: Evidence-enhanced Document-level Relation Extraction | | Yiqing Xie
; Jiaming Shen
; Sha Li
; Yuning Mao
; Jiawei Han
; | | Date: |
16 Jun 2021 | | 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 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 0.76/0.82 Ign F1/F1. In particular, our
method significantly improves the performance on inter-sentence relations by
1.23 Inter F1. | | Source: | arXiv, 2106.08657 | | Services: | Forum | Review | PDF | Favorites | |
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