Stylized Dialogue Generation
Shih‐Wen Ke, Weiliang Chen · 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) · 2021
Dialogue systems such as intelligent online customer services, online chatbots or smart kiosks are becoming increasingly popular. Currently dialogue systems lack personality and ability to respond according to contexts. In this study, we propose an approach to transfer the text into multiple styles when generating dialogue responses. It is especially challenging to build a stylized dialogue system as it combines supervised and unsupervised tasks. In practice, the dialogue data are usually paired, i.e. query paired response while styled text is not. Therefore, we propose using lightweight deep neural network models to bridge the dialogue response generation model and the style transfer model. This structure allows the model to generate responses of different styles to the same query. Our approach will be evaluated against selected state-of-the-art dialogue generation and style transfer techniques.