CauESC：一种用于情感支持对话的因果感知模型
CauESC: A Causal Aware Model for Emotional Support Conversation
AI总结：
针对现有情感支持对话方法忽略情绪成因、未关注互动情绪动态的问题，本文提出因果感知框架CauESC，通过识别情绪成因与效应、独立并整合言语疏导策略，在基准数据集上验证了有效性。
摘要：
情感支持对话旨在通过支持性回应缓解求助者的情绪困扰。现有方法存在两个局限：（1）它们忽略了困扰的情绪成因，而这对细粒度情绪理解至关重要；（2）它们仅关注求助者自身的心理状态，而非对话双方互动过程中的情绪动态变化。为解决这些问题，我们提出了一个新颖的框架CauESC，该框架首先识别困扰的情绪成因以及由这些成因引发的情绪效应，随后独立理解每一种言语疏导策略并将其巧妙整合。在基准数据集上的实验结果证明了我们方法的有效性，同时展现了从成因到效应的情绪理解以及“独立-整合”式策略建模的优势。
英文摘要：
Emotional Support Conversation aims at reducing the seeker's emotional distress through supportive response. Existing approaches have two limitations: (1) They ignore the emotion causes of the distress, which is important for fine-grained emotion understanding; (2) They focus on the seeker's own mental state rather than the emotional dynamics during interaction between speakers. To address these issues, we propose a novel framework CauESC, which firstly recognizes the emotion causes of the distress, as well as the emotion effects triggered by the causes, and then understands each strategy of verbal grooming independently and integrates them skillfully. Experimental results on the benchmark dataset demonstrate the effectiveness of our approach and show the benefits of emotion understanding from cause to effect and independent-integrated strategy modeling.
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。
