WANG Hongjie1,2, YIN Aiying2,3, YE Chenglong1,2, WU Yunbing1,2
1. College of Computer and Data Science, Fuzhou University, Fuzhou 350108; 2. Digital Fujian Institute of Financial Big Data, Fuzhou University, Fuzhou 350108; 3. Department of Computer Engineering, Zhicheng College, Fu-zhou University, Fuzhou 350002
Abstract：Empathetic response generation is aimed at understanding user emotions and generating appropriate responses. The influence of personality traits on empathetic expression is seldom considered in existing methods. As a result, generated responses are likely to be limited in single style and insufficient personality. To address this problem, a personality-fused empathetic response generation model(PFEG) is proposed in this paper. First, a personality-enhanced encoding module is designed to effectively utilize personality information. The personality traits of both interlocutors are obtained and independently adapted and stylized. Then, an iterative reasoning and personality modulation module is constructed to deepen situational understanding. The influence intensity of personality in the current situation is dynamically calculated according to the personality traits of both interlocutors. The emotional tendency and language style of the response are adjusted accordingly. An emotion prediction module is introduced to accurately perceive the potential emotions of the user. Finally, in the personalized gated decoding module, situation, emotion, knowledge, and personality information are effectively fused through a gated integration mechanism. Responses that conform to individual traits and demonstrate deep empathy are generated. Experiments on public datasets show that PFEG outperforms baseline models on multiple metrics.
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2026, Vol. 39 
