Empathetic Response Generation via Emotion Cause Transition Graph
- URL: http://arxiv.org/abs/2302.11787v1
- Date: Thu, 23 Feb 2023 05:51:17 GMT
- Title: Empathetic Response Generation via Emotion Cause Transition Graph
- Authors: Yushan Qian, Bo Wang, Ting-En Lin, Yinhe Zheng, Ying Zhu, Dongming
Zhao, Yuexian Hou, Yuchuan Wu, Yongbin Li
- Abstract summary: Empathetic dialogue is a human-like behavior that requires the perception of both affective factors (e.g., emotion status) and cognitive factors (e.g., cause of the emotion)
We propose an emotion cause transition graph to explicitly model the natural transition of emotion causes between two adjacent turns in empathetic dialogue.
With this graph, the concept words of the emotion causes in the next turn can be predicted and used by a specifically designed concept-aware decoder to generate the empathic response.
- Score: 29.418144401849194
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Empathetic dialogue is a human-like behavior that requires the perception of
both affective factors (e.g., emotion status) and cognitive factors (e.g.,
cause of the emotion). Besides concerning emotion status in early work, the
latest approaches study emotion causes in empathetic dialogue. These approaches
focus on understanding and duplicating emotion causes in the context to show
empathy for the speaker. However, instead of only repeating the contextual
causes, the real empathic response often demonstrate a logical and
emotion-centered transition from the causes in the context to those in the
responses. In this work, we propose an emotion cause transition graph to
explicitly model the natural transition of emotion causes between two adjacent
turns in empathetic dialogue. With this graph, the concept words of the emotion
causes in the next turn can be predicted and used by a specifically designed
concept-aware decoder to generate the empathic response. Automatic and human
experimental results on the benchmark dataset demonstrate that our method
produces more empathetic, coherent, informative, and specific responses than
existing models.
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