Listening-oriented response generation by exploiting user responses

Abstract

Although listening to a conversation partner is a key factor in the success of dialogue systems or conversational agents, recent neural conversation systems have no interest in generating listening-oriented responses. In this paper, we propose an end-to-end dialogue system that generates listening-oriented responses, which make users disclose themselves and feel positive emotions. Our model uses 'self disclosure' and 'positiveness' as listening features and generate responses in an appropriate manner to the features. Furthermore, the model infers a user response that will be brought out at the end of the dialogue and uses the inferred user response for generating a system response. By utilizing both listening features and user responses, our model becomes capable of generating listening-oriented responses. In quantitative and qualitative experiments, our model turned out to be capable of generating listening oriented responses that induce users to disclose themselves and talk positively. The results also show that the model utilizing user responses generates more listening-oriented responses than those only using listening features. (C) 2020 Elsevier B.V. All rights reserved.