Generating Emotional Social Chatbot Responses with a Consistent Speaking Style

Jun Zhang, Yan Yang, Chengcai Chen, Liang He, Zhou Yu

Abstract: Emotional conversation plays a vital role in creating more human-like conversations. Although previous works on emotional conversation generation have achieved promising results, the issue of the speaking style inconsistency still exists. In this paper, we propose a Style-Aware Emotional Dialogue System (SEDS) to enhance speaking style consistency through detecting user’s emotions and modeling speaking styles in emotional response generation. Specifically, SEDS uses an emotion encoder to perceive the user’s emotion from multimodal inputs, and tracks speaking styles through jointly optimizing a generator that is augmented with a personalized lexicon to capture explicit word-level speaking style features. Additionally, we propose an auxiliary task, a speaking style classification task, to guide SEDS to learn the implicit form of speaking style during the training process. We construct a multimodal dialogue dataset and make the alignment and annotation to verify the effectiveness of the model. Experimental results show that our SEDS achieves a significant improvement over other strong baseline models in terms of perplexity, emotion accuracy and style consistency.