DialoguePFM:Prompt-based Fusion Model for Emotion Recognition in Conversation
Yu Tian, Junhui Li, Suyang Zhu, Guodong Zhou · ACM Transactions on Asian and Low-Resource Language Information Processing · 2025
Emotion recognition in conversation (ERC) presents a significant challenge in natural language processing. In this study, we propose the Prompt-based Fusion Model (DialoguePFM), which innovatively introduces an emotion representation that conveys the emotion label via mask token in a pre-defined prompt. We then refine both emotion and utterance representations by capturing comprehensive dialogue information using a novel speaker-aware attention mechanism, which distinguishes between self and other speakers. Subsequently, these refined representations are merged before being inputted into the classifier. Empirical evaluations conducted on three English ERC datasets and one Chinese ERC dataset reveal that our proposed model either outperforms or matches the performance of state-of-the-art baselines, underscoring its effectiveness across different languages.