  The ability of a dialog system to express prespecified language style during
conversations has a direct, positive impact on its usability and on user
satisfaction. We introduce a new prototype-to-style (PS) framework to tackle
the challenge of stylistic dialogue generation. The framework uses an
Information Retrieval (IR) system and extracts a response prototype from the
retrieved response. A stylistic response generator then takes the prototype and
the desired language style as model input to obtain a high-quality and
stylistic response. To effectively train the proposed model, we propose a new
style-aware learning objective as well as a de-noising learning strategy.
Results on three benchmark datasets from two languages demonstrate that the
proposed approach significantly outperforms existing baselines in both
in-domain and cross-domain evaluations

Computation and Language