Stylistic Dialogue Generation via Information-Guided Reinforcement Learning Strategy
This addresses the challenge of balancing style and content in dialogue systems for industrial applications, but it appears incremental as it builds on existing reinforcement learning methods.
The paper tackles the problem of generating stylistic dialogue responses without sacrificing content quality by introducing Information-Guided Reinforcement Learning (IG-RL), which outperforms strong baselines on two datasets.
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.