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.

尽管倾听对话伙伴是对话系统或对话代理成功的关键因素，但最近的神经对话系统对生成面向聆听的响应没有兴趣。在本文中，我们提出了一种端到端对话系统，该系统可以生成面向聆听的响应，使用户能够自我展示并感受到积极的情绪。我们的模型使用“自我披露”和“积极性”作为聆听特征，并以适当的方式生成对特征的响应。此外，该模型推断将在对话结束时带出的用户响应，并使用推断的用户响应来生成系统响应。通过同时利用收听功能和用户响应，我们的模型能够生成面向收听的响应。在定量和定性实验中，我们的模型能够生成面向聆听的响应，从而诱使用户公开自我并积极交谈。结果还表明，与仅使用侦听功能的模型相比，利用用户响应的模型生成的侦听响应更多。