EmoDynamiX：通过建模混合情绪与话语动态预测情感支持对话策略
EmoDynamiX: Emotional Support Dialogue Strategy Prediction by Modelling MiXed Emotions and Discourse Dynamics
AI总结：
针对LLMs端到端情感支持对话系统策略不透明及偏好偏差问题，提出EmoDynamiX框架，将策略预测与生成解耦，建模用户细粒度情绪与系统策略的话语动态，提升性能与透明度。
摘要：
设计具有情绪智能的对话系统，为处于困境中的人提供安慰和建议是一个极具吸引力的研究领域。近年来，随着大型语言模型（LLMs）的发展，无需显式策略预测步骤的端到端对话智能体变得普遍。然而，隐式策略规划缺乏透明度，且近期研究表明LLMs对某些社会情绪策略固有的偏好偏差会阻碍高质量情感支持的传递。为应对这一挑战，我们提出将策略预测与语言生成解耦，并引入一种新型对话策略预测框架EmoDynamiX，该框架使用异构图对用户细粒度情绪与系统策略之间的话语动态进行建模，以实现更好的性能和透明度。在两个ESC数据集上的实验结果显示，EmoDynamiX显著优于此前的最先进方法（更高的熟练度和更低的偏好偏差）。我们的方法还通过允许回溯决策过程展现出更好的透明度。
英文摘要：
Designing emotionally intelligent conversational systems to provide comfort and advice to people experiencing distress is a compelling area of research. Recently, with advancements in large language models (LLMs), end-to-end dialogue agents without explicit strategy prediction steps have become prevalent. However, implicit strategy planning lacks transparency, and recent studies show that LLMs' inherent preference bias towards certain socio-emotional strategies hinders the delivery of high-quality emotional support. To address this challenge, we propose decoupling strategy prediction from language generation, and introduce a novel dialogue strategy prediction framework, EmoDynamiX, which models the discourse dynamics between user fine-grained emotions and system strategies using a heterogeneous graph for better performance and transparency. Experimental results on two ESC datasets show EmoDynamiX outperforms previous state-of-the-art methods with a significant margin (better proficiency and lower preference bias). Our approach also exhibits better transparency by allowing backtracing of decision making.
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。
