  Stylistic response generation is crucial for building an engaging dialogue
system for industrial use. While it has attracted much research interest,
existing methods often generate stylistic responses at the cost of the content
quality (relevance and fluency). To enable better balance between the content
quality and the style, we introduce a new training strategy, know as
Information-Guided Reinforcement Learning (IG-RL). In IG-RL, a training model
is encouraged to explore stylistic expressions while being constrained to
maintain its content quality. This is achieved by adopting reinforcement
learning strategy with statistical style information guidance for
quality-preserving explorations. Experiments on two datasets show that the
proposed approach outperforms several strong baselines in terms of the overall
response performance.
