Generating Persona-Aware Empathetic Responses with Retrieval-Augmented Prompt Learning

Empathetic response generation requires perceiving and understanding the user’s emotion to deliver suitable responses. However, existing models generally lack an ability to respond in a persona-specific way, which has been shown to play a vital role in expressing appropriate empathy. To address this problem, we propose a novel Transformer-based architecture that incorporates retrieval-augmented prompt learning to generate persona-aware empathetic responses. Since personalized emotional resonance is subtle and uncontrollable, we employ dense passage retrieval to retrieve exemplary responses that reflect specific persona and context characteristics to cue the generative model on signaling empathy. Extensive experiments confirm the effectiveness of our model for persona-aware empathetic response generation.