Predicting Emotion Reactions for Human-Computer Conversation: A Variational Approach

Rui Zhang, Zhenyu Wang, Zhenhua Huang, Li Li, Mengdan Zheng

Abstract: Although emotional conversation generation has attracted widespread attention in recent years, research works on the emotional interactional mechanism are still scarce, which makes it difficult for existing emotional dialogue system to automatically determine a suitable emotion type for conversation generation. Such a response emotion planning task is often difficult due to the “gap” problem: we need to predict the emotional probability distribution of the upcoming responses, which have not yet been generated. In this article, we propose a novel method, namely interactional emotion learning (IEL), which adopts an intuitive way to eliminate the “gap” problem: we design a variational learning network, called potential response learning, to learn the latent distribution of responses for given conversation in a semantic space. Then, we predict an appropriate emotion for response generation based on both the dialogue context and the learned latent distribution. Extensive experiments have been performed on three off-the-shelf conversation datasets, and the experimental results show that the proposed variational learning network significantly boosts the prediction ability of our approach, and our IEL method outperforms the state-of-the-art dialogue classification methods in the emotion planning task.