  The ability of a dialog system to express consistent language style during
conversations has a direct, positive impact on its usability and on user
satisfaction. Although previous studies have demonstrated that style transfer
is feasible with a large amount of parallel data, it is often impossible to
collect such data for different styles. In this paper, instead of manually
constructing conversation data with a certain style, we propose a flexible
framework that adapts a generic retrieval-based dialogue system to mimic the
language style of a specified persona without any parallel data. Our approach
is based on automatic generation of stylized data by learning the usage of
jargon, and then rewriting the generic conversations to a stylized one by
incorporating the jargon. In experiments we implemented dialogue systems with
five distinct language styles, and the result shows our framework significantly
outperforms baselines in terms of the average score of responses' relevance and
style degree, and content diversity. A/B testing on a commercial chatbot shows
that users are more satisfied with our system. This study demonstrates the
feasibility of building stylistic dialogue systems by simple data augmentation.
