Dialogue relation extraction (DRE) aims to detect the relation between two
entities mentioned in a multi-party dialogue. It plays an important role in
constructing knowledge graphs from conversational data increasingly abundant on
the internet and facilitating intelligent dialogue system development. The
prior methods of DRE do not meaningfully leverage speaker information-they just
prepend the utterances with the respective speaker names. Thus, they fail to
model the crucial inter-speaker relations that may give additional context to
relevant argument entities through pronouns and triggers. We, however, present
a graph attention network-based method for DRE where a graph, that contains
meaningfully connected speaker, entity, entity-type, and utterance nodes, is
constructed. This graph is fed to a graph attention network for context
propagation among relevant nodes, which effectively captures the dialogue
context. We empirically show that this graph-based approach quite effectively
captures the relations between different entity pairs in a dialogue as it
outperforms the state-of-the-art approaches by a significant margin on the
benchmark dataset DialogRE. Our code is released at:
this https URL

本研究提出了一种基于图注意力网络的对话关系提取方法，该方法能够有效捕捉对话中不同实体对之间的关系和解释人际关系，从而使得对话数据更好地用于构建知识图谱和开发智能对话系统。