Emotional Support Conversation (ESC) systems are pivotal in providing empathetic interactions, aiding users through negative emotional states by understanding and addressing their unique experiences. In this paper, we tackle two key challenges in ESC: enhancing contextually relevant and empathetic response generation through dynamic demonstration retrieval, and advancing cognitive understanding to grasp implicit mental states comprehensively. We introduce Dynamic Demonstration Retrieval and Cognitive-Aspect Situation Understanding (\ourwork), a novel approach that synergizes these elements to improve the quality of support provided in ESCs. By leveraging in-context learning and persona information, we introduce an innovative retrieval mechanism that selects informative and personalized demonstration pairs. We also propose a cognitive understanding module that utilizes four cognitive relationships from the ATOMIC knowledge source to deepen situational awareness of help-seekers' mental states. Our supportive decoder integrates information from diverse knowledge sources, underpinning response generation that is both empathetic and cognitively aware. The effectiveness of \ourwork is demonstrated through extensive automatic and human evaluations, revealing substantial improvements over numerous state-of-the-art models, with up to 13.79\% enhancement in overall performance of ten metrics. Our codes are available for public access to facilitate further research and development.

翻译：情感支持对话（ESC）系统在提供共情交互中至关重要，通过理解并应对用户的独特体验，帮助其摆脱负面情绪状态。本文聚焦于ESC中的两个关键挑战：通过动态演示检索增强上下文相关且富有共情的回应生成，以及提升认知理解能力以全面把握隐含的心理状态。我们提出动态演示检索与认知情境理解方法（\ourwork），该方法协同整合这些要素，以提升ESC中支持质量。通过利用上下文学习和人格信息，我们引入一种创新检索机制，选取信息丰富且个性化强的演示对。此外，我们提出认知理解模块，利用ATOMIC知识源中的四种认知关系，加深对求助者心理状态的情境感知。我们的支持性解码器整合了多种知识源的信息，为生成既具共情性又具认知感知的回应奠定基础。通过广泛的自动评估与人工评估，证明了\ourwork的有效性，其在十个指标的整体性能上相较于众多最先进模型实现了高达13.79%的提升。我们公开了代码，以促进进一步的研究与发展。