Dialogue relation extraction (DRE) that identifies the relations between argument pairs in dialogue text, suffers much from the frequent occurrence of personal pronouns, or entity and speaker coreference. This work introduces a new benchmark dataset DialogRE^C+, introducing coreference resolution into the DRE scenario. With the aid of high-quality coreference knowledge, the reasoning of argument relations is expected to be enhanced. In DialogRE^C+ dataset, we manually annotate total 5,068 coreference chains over 36,369 argument mentions based on the existing DialogRE data, where four different coreference chain types namely speaker chain, person chain, location chain and organization chain are explicitly marked. We further develop 4 coreference-enhanced graph-based DRE models, which learn effective coreference representations for improving the DRE task. We also train a coreference resolution model based on our annotations and evaluate the effect of automatically extracted coreference chains demonstrating the practicality of our dataset and its potential to other domains and tasks.

翻译：对话关系抽取旨在识别对话文本中论元对之间的语义关系，但频繁出现的人称代词以及实体与说话人的共指现象严重制约了其性能。本文提出新型基准数据集DialogRE^C+，将共指消解引入对话关系抽取场景，期望通过高质量共指知识增强论元关系的推理能力。我们在现有DialogRE数据基础上，对36,369个论元提及手工标注了总计5,068条共指链，明确区分说话人链、人物链、地点链与组织链四种共指链类型。进一步构建四种基于图的共指增强对话关系抽取模型，通过学习有效共指表征提升对话关系抽取性能。同时基于标注数据训练共指消解模型，评估自动抽取共指链的实际效果，验证了本数据集的有效性及其向其他领域和任务迁移的潜力。