An Embarrassingly Simple Model for Dialogue Relation Extraction

• URL: http://arxiv.org/abs/2012.13873v1
• Date: Sun, 27 Dec 2020 06:22:23 GMT
• Title: An Embarrassingly Simple Model for Dialogue Relation Extraction
• Authors: Fuzhao Xue, Aixin Sun, Hao Zhang, Eng Siong Chng
• Abstract summary: 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) A Relation Refinement Gate (RRG) is designed to extract relation-specific semantic representation adaptively.
• Score: 44.2379205657313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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