Emotion-and-knowledge grounded response generation in an open-domain dialogue setting

The neural-based interactive dialogue system focuses on engaging and retaining humans in long-lasting conversations. This has been explored for a variety of goal-oriented dialogue domains, such as education, health care, entertainment, sports, and politics. To develop an understanding and awareness of social and cultural norms, and to address specific social skills, we need to invent strategies for building interactive systems that take into account the user's emotions and relevant-facts in a multi-turn conversation. In this paper, we propose a new neural generative model that combines step-wise co-attention with a self-attention-based transformer network along with an emotion classifier to jointly control emotion and knowledge transfer during response generation. Quantitative, qualitative, and human evaluation results on the benchmark Topical Chat and the CMU_DoG dataset show that the proposed models can generate natural and coherent sentences, capturing essential facts with considerable improvement over emotional content.

Automated summary

This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.