A Neural Generation-based Conversation Model Using Fine-grained Emotion-guide Attention

Human emotion interaction is crucial to social communications. However, existing generation-based conversation systems mainly put emphasis on the content of responses in terms of naturalness, diversity and coherence without consideration of the emotion interaction between conversation. In order to reduce the gap between human-generated and computer-generated responses, in this work we present a human-like Emotional Conversation Generation Model, named ECGM, by imitating human conversation. Specifically, ECGM applies an emotion-guide attention which captures and integrates the emotion of the given post into neural response generation. Comparative experiments evaluated by computerised and manual methods show that our proposed model is capable of generating more human-like emotional responses and relevant content as well.