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The ability of a dialog system to express consistent language style duringconversations has a direct, positive impact on its usability and on usersatisfaction. Although previous studies have demonstrated that style transferis feasible with a large amount of parallel data, it is often impossible tocollect such data for different styles. In this paper, instead of manuallyconstructing conversation data with a certain style, we propose a flexibleframework that adapts a generic retrieval-based dialogue system to mimic thelanguage style of a specified persona without any parallel data. Our approachis based on automatic generation of stylized data by learning the usage ofjargon, and then rewriting the generic conversations to a stylized one byincorporating the jargon. In experiments we implemented dialogue systems withfive distinct language styles, and the result shows our framework significantlyoutperforms baselines in terms of the average score of responses' relevance andstyle degree, and content diversity. A/B testing on a commercial chatbot showsthat users are more satisfied with our system. This study demonstrates thefeasibility of building stylistic dialogue systems by simple data augmentation.

Stylistic Retrieval-based Dialogue System with Unparallel Training Data