Dual variational generative model and auxiliary retrieval for empathetic response generation by conversational robot

Abstract

Empathy in human-robot conversations aims to endow the robot with the ability to comprehend user emotion and experience, and then respond to it appropriately. Generally, empathy is embodied in the aspects of both contextual understanding and affective expression, which occur when there exist content and emotion consistencies between context and response. However, previous studies only focus on either aspect. In this paper, we propose a dual variational generative model (DVG) for empathetic response generation to achieve both. Specifically, we integrate an emotion classifier and a variational autoencoder (VAE) into a dual response and context generative model to learn the emotion and content consistencies efficiently. DVG utilizes VAE to mimic the process of context/response understanding. In addition to the generative model, our model can effectively switch to another retrieval system as a fallback solution. Automatic and human evaluations on Japanese and English EmpatheticDialogue datasets demonstrate the effectiveness of our method for empathetic response generation. Furthermore, we evaluate our model's ability in general response generation, which is not specific to empathetic but also chitchatting dialogue system.