Emotional support conversation (ESC) aims to provide emotional support (ES) to improve one's mental state. Existing works stay at fitting grounded responses and responding strategies (e.g., question), which ignore the effect on ES and lack explicit goals to guide emotional positive transition. To this end, we introduce a new paradigm to formalize multi-turn ESC as a process of positive emotion elicitation. Addressing this task requires finely adjusting the elicitation intensity in ES as the conversation progresses while maintaining conversational goals like coherence. In this paper, we propose Supporter, a mixture-of-expert-based reinforcement learning model, and well design ES and dialogue coherence rewards to guide policy's learning for responding. Experiments verify the superiority of Supporter in achieving positive emotion elicitation during responding while maintaining conversational goals including coherence.

翻译：情感支持对话旨在通过提供情感支持来改善个体的心理状态。现有研究侧重于生成符合语境的回应与策略（如提问），却忽视了情感支持的实际效果，且缺乏引导情绪正向转变的明确目标。为此，我们提出一种新范式，将多轮情感支持对话形式化为积极情绪诱发过程。完成该任务需要在对话推进中精细调节情感支持的诱发强度，同时维持连贯性等对话目标。本文提出Supporter——一种基于混合专家模型的强化学习方法，并精心设计了情感支持与对话连贯性奖励，以指导策略学习生成回应。实验证明，Supporter在维持对话连贯性等目标的同时，能够在回应过程中实现积极情绪诱发，展现出优越性能。