面向情感支持对话的动态示范检索与认知理解
Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation
AI总结：
针对情感支持对话，本文提出\ourwork，通过个性化动态示范检索和基于ATOMIC四类认知关系的情境理解，生成更具共情与认知感知的回复，并在十项指标上最高提升13.79%。
摘要：
情感支持对话（Emotional Support Conversation，ESC）系统在提供共情互动方面至关重要，可通过理解并回应求助者的独特经历，帮助其度过负面情绪状态。本文解决ESC中的两个关键挑战：通过动态示范检索增强与情境相关且具有共情性的回复生成，以及推进认知理解以全面把握隐含心理状态。我们提出Dynamic Demonstration Retrieval and Cognitive-Aspect Situation Understanding（\ourwork），这是一种将上述要素协同结合的新方法，用于提升ESC中所提供支持的质量。通过利用上下文学习和角色信息，我们引入了一种创新检索机制，用于选择信息丰富且个性化的示范对。我们还提出一个认知理解模块，该模块利用来自ATOMIC知识源的四种认知关系，以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来自多种知识源的信息，为兼具共情性和认知感知能力的回复生成提供支撑。通过大量自动评估和人工评估，\ourwork的有效性得到证明；结果显示，其相较众多最先进模型取得显著提升，在十项指标的总体性能上提升最高达13.79%。我们的代码已公开，以促进进一步的研究与开发。
英文摘要：
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.
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。
