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.

本文提出了一种名为 SimpleRE 的简单而有效的对话关系提取模型，通过一种新颖的输入格式 BRS 捕捉了对话中多个关系间的相互关联，并通过关系细化门（RRG）以自适应的方式提取关系特定的语义表示。实验表明，SimpleRE 在 DialogRE 数据集上取得了最好的性能，并且没有使用外部资源就优于所有直接基线在句级关系提取上的表现。