The existing emotion-cause pair extraction (ECPE) task, unfortunately,
ignores extracting the emotion type and cause type, while these fine-grained
meta-information can be practically useful in real-world applications, i.e.,
chat robots and empathic dialog generation. Also the current ECPE is limited to
the scenario of single text piece, while neglecting the studies at dialog level
that should have more realistic values. In this paper, we extend the ECPE task
with a broader definition and scenario, presenting a new task, Emotion-Cause
Quadruple Extraction in Dialogs (ECQED), which requires detecting emotion-cause
utterance pairs and emotion and cause types. We present an ECQED model based on
a structural and semantic heterogeneous graph as well as a parallel grid
tagging scheme, which advances in effectively incorporating the dialog context
structure, meanwhile solving the challenging overlapped quadruple issue. Via
experiments we show that introducing the fine-grained emotion and cause
features evidently helps better dialog generation. Also our proposed ECQED
system shows exceptional superiority over baselines on both the emotion-cause
quadruple or pair extraction tasks, meanwhile being highly efficient.

本文提出了 Emotion-Cause Quadruple Extraction in Dialogs (ECQED) 任务，旨在检测情感 - 原因话语对和情感与原因类型。我们提出了一种基于结构和语义异构图及并行网格标记方案的 ECQED 模型，通过实验表明引入细粒度的情感和原因特征可显著提高对话生成的性能，并且我们的 ECQED 系统在情感 - 原因四元组或对提取任务上表现出了显著的优越性，同时高效。