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Emotion recognition in conversation, which aims to predict the emotion forall utterances, has attracted considerable research attention in recent years.It is a challenging task since the recognition of the emotion in one utteranceinvolves many complex factors, such as the conversational context, thespeaker's background, and the subtle difference between emotion labels. In thispaper, we propose a novel framework which mimics the thinking process whenmodeling these factors. Specifically, we first comprehend the conversationalcontext with a history-oriented prompt to selectively gather information frompredecessors of the target utterance. We then model the speaker's backgroundwith an experience-oriented prompt to retrieve the similar utterances from allconversations. We finally differentiate the subtle label semantics with aparaphrasing mechanism to elicit the intrinsic label related knowledge. Weconducted extensive experiments on three benchmarks. The empirical resultsdemonstrate the superiority of our proposed framework over the state-of-the-artbaselines.

Mimicking the Thinking Process for Emotion Recognition in Conversation with Prompts and Paraphrasing