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An Embarrassingly Simple Model for Dialogue Relation Extraction

2020-12-27 06:22:23
Fuzhao Xue, Aixin Sun, Hao Zhang, Eng Siong Chng

Abstract

Dialogue relation extraction (RE) is to predict the relation type of two entities mentioned in a dialogue. In this paper, we model Dialogue RE as a multi-label classification task and propose a simple yet effective model named SimpleRE. SimpleRE captures the interrelations among multiple relations in a dialogue through a novel input format, BERT Relation Token Sequence (BRS). In BRS, multiple [CLS] tokens are used to capture different relations between different pairs of entities. A Relation Refinement Gate (RRG) is designed to extract relation-specific semantic representation adaptively. Experiments on DialogRE show that SimpleRE achieves the best performance with much shorter training time. SimpleRE outperforms all direct baselines on sentence-level RE without using external resources.

Abstract (translated)

URL

https://arxiv.org/abs/2012.13873

PDF

https://arxiv.org/pdf/2012.13873.pdf


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