Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning
It addresses the challenge of providing effective emotional support in multi-turn conversations for users in emotional distress, representing an incremental improvement over single-turn approaches.
The paper tackled the problem of building multi-turn emotional support dialogue systems by proposing MultiESC, which uses lookahead strategy planning and dynamic user state modeling, resulting in significant outperformance over baselines in dialogue generation and strategy planning.
Providing Emotional Support (ES) to soothe people in emotional distress is an essential capability in social interactions. Most existing researches on building ES conversation systems only considered single-turn interactions with users, which was over-simplified. In comparison, multi-turn ES conversation systems can provide ES more effectively, but face several new technical challenges, including: (1) how to adopt appropriate support strategies to achieve the long-term dialogue goal of comforting the user's emotion; (2) how to dynamically model the user's state. In this paper, we propose a novel system 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 dialogue generation and strategy planning. Our codes are available at https://github.com/lwgkzl/MultiESC.