通过积极情绪唤起促进多轮情感支持对话：一种强化学习方法
Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach
AI总结：
针对多轮情感支持对话缺乏明确情绪改善目标的问题，提出基于混合专家强化学习的Supporter模型，通过情感支持与连贯性奖励引导回应策略，有效唤起积极情绪并保持对话连贯。
摘要：
情感支持对话（ESC）旨在提供情感支持（ES）以改善个体的心理状态。现有工作停留在拟合有依据的回复和回应策略（例如提问），忽视了其对情感支持的效果，并且缺乏明确目标来引导情绪的积极转变。为此，我们引入一种新范式，将多轮ESC形式化为积极情绪唤起的过程。完成该任务需要随着对话推进精细调整情感支持中的唤起强度，同时保持连贯性等对话目标。本文提出Supporter，这是一种基于混合专家的强化学习模型，并精心设计情感支持奖励和对话连贯性奖励，以引导策略学习如何回应。实验验证了Supporter在回应过程中实现积极情绪唤起、同时保持包括连贯性在内的对话目标方面的优越性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
