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