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Emotion recognition in conversation (ERC) aims to detect the emotion for eachutterance in a given conversation. The newly proposed ERC models have leveragedpre-trained language models (PLMs) with the paradigm of pre-training andfine-tuning to obtain good performance. However, these models seldom exploitPLMs' advantages thoroughly, and perform poorly for the conversations lackingexplicit emotional expressions. In order to fully leverage the latent knowledgerelated to the emotional expressions in utterances, we propose a novel ERCmodel CISPER with the new paradigm of prompt and language model (LM) tuning.Specifically, CISPER is equipped with the prompt blending the contextualinformation and commonsense related to the interlocutor's utterances, toachieve ERC more effectively. Our extensive experiments demonstrate CISPER'ssuperior performance over the state-of-the-art ERC models, and theeffectiveness of leveraging these two kinds of significant prompt informationfor performance gains. To reproduce our experimental results conveniently,CISPER's sourcecode and the datasets have been shared athttps://github.com/DeqingYang/CISPER.

Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversation