A Control Unit for Emotional Conversation Generation

Emotional conversation generation model predicts the response according to the current words and the emotional words. However, the researchers only dedicated to adding more emotional words in the conversation generation model to retain the taste of chat users without considering whether the emotion of a response is suitable for human conversations or not. In this paper, we aim to address the issue of emotion drift which indicates the emotion of a response is not the same category as its post in human conversations. We propose a control unit framework, which consists of emotional channels and word-level attention mechanism, to incorporate natural and smooth emotional words into conversation generation. Emotional channel consists six channels, namely like, sadness, disgust, anger, happiness and other ones, which provides strategy choice control unit to generate emotional words. To improve the importance of emotional content, we use the word-level attention mechanism in emotional channel for acquiring a better emotional decoding response. Experimental results suggest that the proposed model is effective not only in generate content but also in emotion.