论文标题
对话关系提取的令人尴尬的简单模型
An Embarrassingly Simple Model for Dialogue Relation Extraction
论文作者
论文摘要
对话关系提取（RE）是为了预测对话中提到的两个实体的关系类型。在本文中，我们为RE任务提出了一个名为Simpleere的简单而有效的模型。 Simpleere通过名为BERT关系令牌序列（BRS）的新颖输入格式在对话中捕获了多个关系之间的相互关系。在BRS中，多个[Cls]令牌用于捕获对话中提到的不同实体之间的可能关系。然后，将设计关系改进门（RRG）以自适应方式提取特定于关系的语义表示。对话框数据集的实验表明，Simplere在训练时间较短的时间内实现了最佳性能。此外，Simplere在句子级别的RE上的所有直接基线都不使用外部资源。
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.
