Empathetic Response Generation Based on Emotional Transition Prompt and Dual-Semantic Contrastive Learning

Empathetic response generation stands as a pivotal endeavor in the development of human-like dialogue systems. An effective approach in previous research is integrating external knowledge to generate empathetic responses. However, existing approaches only focus on identifying a user’s current emotional state, and they overlook the user’s emotional transition during context, and fail to propel the sustainability of the dialogue. To tackle the aforementioned issues, we propose an empathetic response generation model based on an emotional transition prompt and dual-semantic contrastive learning (EPDC). Specifically, we first compute the transition in users’ sentiment polarity during the conversation and incorporate it into the conversation embedding as sentiment prompts. Then, we generate two distinct fine-grained contextual representations and treat them as positive examples for contrastive learning, respectively, aiming at extracting high-order semantic information to guide the subsequent turn of dialogue. Finally, we also leverage commonsense knowledge to enhance the contextual representations, and the empathetic responses are generated by decoding the combination of semantic and emotional states. Notably, our work represents the pioneering application of emotional prompts and contrastive learning to augment the sustainability of empathetic dialogue. Extensive experiments conducted on the benchmark dataset EMPATHETICDIALOGUES demonstrate that EPDC outperforms the baselines in both automatic evaluations and human evaluations.