The goal of dialogue relation extraction (DRE) is to identify the relation between two entities in a given dialogue. During conversations, speakers may expose their relations to certain entities by explicit or implicit clues, such evidences called "triggers". However, trigger annotations may not be always available for the target data, so it is challenging to leverage such information for enhancing the performance. Therefore, this paper proposes to learn how to identify triggers from the data with trigger annotations and then transfers the trigger-finding capability to other datasets for better performance. The experiments show that the proposed approach is capable of improving relation extraction performance of unseen relations and also demonstrate the transferability of our proposed trigger-finding model across different domains and datasets.

翻译：对话关系提取(DRE)的目标是在特定对话中确定两个实体之间的关系。在对话中,发言者可能通过明示或暗示的线索暴露他们与某些实体的关系,例如被称为“触发器”的证据。然而,目标数据不一定总能提供触发说明,因此,利用这类信息提高性能具有挑战性,因此,本文件提议学习如何识别带有触发说明的数据触发器,然后将触发调查能力转移到其他数据集,以便更好的性能。实验表明,拟议的方法能够改进隐蔽关系的关系提取关系的关系,并表明我们提议的触发调查模式在不同领域和数据集之间的可转让性。