Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning

Providing Emotional Support (ES) to soothe people in emotional distress is an essential capability in social interactions. Most existing research on building ES conversation systems only considers single-turn interactions with users, which is over-simplified. In comparison, multi-turn ES conversation systems can provide ES more effectively, but face several new technical challenges, including: i) how to conduct support strategy planning that could lead to the best supporting effects; ii) how to dynamically model the user's state. In this paper, we propose a novel system named MultiESC to address these issues. For strategy planning, drawing inspiration from the A* search algorithm, we propose lookahead heuristics to estimate the future user feedback after using particular strategies, which helps to select strategies that can lead to the best long-term effects. For user state modeling, MultiESC focuses on capturing users' subtle emotional expressions and understanding their emotion causes. Extensive experiments show that MultiESC significantly outperforms competitive baselines in both strategy planning and dialogue generation. Our codes are available at https: //github.com/lwgkzl/MultiESC .