  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.
