MLAL: Multiple Prompt Learning and Generation of Auxiliary Labeled Utterances for Emotion Recognition in Conversations

Abstract

Emotion Recognition in Conversations (ERC) is one of the most prominent research directions in the field of Natural Language Processing (NLP). It aims to accurately identify the emotional state expressed in conversations and is widely applied in psychology, education, and healthcare. However, ERC poses significant challenges due to various factors, such as conversational context, the experience of speaker, and subtle differences between similar emotion labels. Existing research primarily strives for effective sequence and graph structure to model utterance and interaction. Moreover, these methods lack comprehensive understanding of conversational contexts and precise distinction between similar emotions. To address the limitation, in this study, we propose a novel framework combining Multiple Prompt Learning and Generation of Auxiliary Labeled Utterances (MLAL). Firstly, a global prompt is constructed to facilitate the understanding of the conversational context. Specifically, utterances originating from the same speaker are identified and interactively processed. Simultaneously, taking into account the influence of speaker experience, an experience prompt is designed by retrieving and interacting with the historical utterances of speakers that display high similarity. Besides, we generate refined auxiliary labeled utterances by means of the label paraphrasing mechanism to distinguish between similar emotions. Results from experiments show that our proposed approach performs better on three datasets than the state-of-the-art techniques currently in use.