CTSM: Combining Trait and State Emotions for Empathetic Response Model
This work addresses the challenge of insufficient emotional perception in empathetic dialogue systems, offering a domain-specific improvement for human-computer interaction.
The paper tackles the problem of generating empathetic responses in dialogue systems by combining both trait (static) and state (dynamic) emotions, which were previously treated in isolation, resulting in improved empathetic expression. It demonstrates that CTSM outperforms state-of-the-art baselines in evaluations.
Empathetic response generation endeavors to empower dialogue systems to perceive speakers' emotions and generate empathetic responses accordingly. Psychological research demonstrates that emotion, as an essential factor in empathy, encompasses trait emotions, which are static and context-independent, and state emotions, which are dynamic and context-dependent. However, previous studies treat them in isolation, leading to insufficient emotional perception of the context, and subsequently, less effective empathetic expression. To address this problem, we propose Combining Trait and State emotions for Empathetic Response Model (CTSM). Specifically, to sufficiently perceive emotions in dialogue, we first construct and encode trait and state emotion embeddings, and then we further enhance emotional perception capability through an emotion guidance module that guides emotion representation. In addition, we propose a cross-contrastive learning decoder to enhance the model's empathetic expression capability by aligning trait and state emotions between generated responses and contexts. Both automatic and manual evaluation results demonstrate that CTSM outperforms state-of-the-art baselines and can generate more empathetic responses. Our code is available at https://github.com/wangyufeng-empty/CTSM