Empathetic Response Generation with State Management
- URL: http://arxiv.org/abs/2205.03676v1
- Date: Sat, 7 May 2022 16:17:28 GMT
- Title: Empathetic Response Generation with State Management
- Authors: Yuhan Liu, Jun Gao, Jiachen Du, Lanjun Zhou, Ruifeng Xu
- Abstract summary: The goal of empathetic response generation is to enhance the ability of dialogue systems to perceive and express emotions in conversations.
We propose a novel empathetic response generation model that can consider multiple state information including emotions and intents simultaneously.
Experimental results show that dynamically managing different information can help the model generate more empathetic responses.
- Score: 32.421924357260075
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The goal of empathetic response generation is to enhance the ability of
dialogue systems to perceive and express emotions in conversations. Current
approaches to this task mainly focus on improving the response generation model
by recognizing the emotion of the user or predicting a target emotion to guide
the generation of responses. Such models only exploit partial information (the
user's emotion or the target emotion used as a guiding signal) and do not
consider multiple information together. In addition to the emotional style of
the response, the intent of the response is also very important for empathetic
responding. Thus, we propose a novel empathetic response generation model that
can consider multiple state information including emotions and intents
simultaneously. Specifically, we introduce a state management method to
dynamically update the dialogue states, in which the user's emotion is first
recognized, then the target emotion and intent are obtained via predefined
shift patterns with the user's emotion as input. The obtained information is
used to control the response generation. Experimental results show that
dynamically managing different information can help the model generate more
empathetic responses compared with several baselines under both automatic and
human evaluations.
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