Stylistically User-specific Response Generation

Abstract

The ability to capture the conversation context is a necessity to build a good conversation model. However, a good model must also provide interesting and diverse responses to mimic actual human conversations. Given that different people can respond differently to the same utterance, we believe that using user-specific attributes can be useful for a conversation task. In this study, we attempt to drive the style of generated responses to resemble the style of real people using user-specific information. Our experiments show that our method applies to both seen and unseen users. Human evaluation also shows that our model outperforms the baselines in terms of relevance and style similarity.