  Current Knowledge-Grounded Dialogue Generation (KDG) models specialize in
producing rational and factual responses. However, to establish long-term
relationships with users, the KDG model needs the capability to generate
responses in a desired style or attribute. Thus, we study a new problem:
Stylized Knowledge-Grounded Dialogue Generation (SKDG). It presents two
challenges: (1) How to train a SKDG model where no <context, knowledge,
stylized response> triples are available. (2) How to cohere with context and
preserve the knowledge when generating a stylized response. In this paper, we
propose a novel disentangled template rewriting (DTR) method which generates
responses via combing disentangled style templates (from monolingual stylized
corpus) and content templates (from KDG corpus). The entire framework is
end-to-end differentiable and learned without supervision. Extensive
experiments on two benchmarks indicate that DTR achieves a significant
improvement on all evaluation metrics compared with previous state-of-the-art
stylized dialogue generation methods. Besides, DTR achieves comparable
performance with the state-of-the-art KDG methods in standard KDG evaluation
setting.
