  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.
