Implicit and Explicit Emotion Enhanced Empathetic Dialogue Generation

Abstract

Empathetic conversation systems identify the users' emotions and give appropriate responses, which is crucial to improve users' experiences. However, existing empathetic dialogue models (especially to the dominant pre-trained language model-based systems) did not focus on modelling the holistic properties of implicit and explicit emotions. In this paper, we propose an Implicit and Explicit Emotion Enhanced (IEEE) empathetic dialogue generation model to handle such challenges. Specifically, we first propose a prompt tuning-based approach to mine emotional words as additional information to obtain the users' explicit emotion. A variational auto-encoder is then introduced to extract the topic words of the input sequence as additional priori knowledge to get the implicit emotion related information. Finally, a pre-trained language model is utilized as the auto-regressive decoder to generate empathetic responses related to the content of the topics and user emotions. To demonstrate the effectiveness of the proposed approach, IEEE has been tested on empathic dialogue dataset. The experimental results show that our method achieves better performance than some competitive models.