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
this https URL.

本文提出了一个使用多轮模式的情感支持会话系统 MultiESC，通过使用前瞻性启发式策略规划和用户状态建模来解决长期对话目标和用户情感的动态模态问题，实证结果表明 MultiESC 在对话生成和策略规划方面均明显优于竞争基线。