用双向认知分析提升情感支持对话的可解释性与效果
Mind2: Mind-to-Mind Emotional Support System with Bidirectional Cognitive Discourse Analysis
- 基于动态话语上下文窗口，双向分析用户与系统认知
- 仅用10%数据训练即达顶尖性能，显著降低资源需求
- 适合需高可信度、可解释性的情感支持场景
情感支持（ES）系统通过生成策略性对话缓解用户心理压力。然而，现有系统在生成及时、可解释的对话方面存在局限，难以获得公众信任。受认知模型启发，我们提出Mind-to-Mind（Mind2）框架，从话语分析视角实现可解释的情感支持上下文建模。具体而言，利用动态话语上下文传播窗口，随对话推进动态捕捉演变中的上下文信息。为增强可解释性，Mind2引入双向认知机制，整合心智理论、生理预期效用与认知理性，提取对话中双方对彼此信念的认知知识。实验表明，Mind2在仅使用10%可用训练数据的情况下，性能媲美当前最优系统。
原文摘要 · Abstract (English)
Emotional support (ES) systems alleviate users' mental distress by generating strategic supportive dialogues based on diverse user situations. However, ES systems are limited in their ability to generate effective ES dialogues that include timely context and interpretability, hindering them from earning public trust. Driven by cognitive models, we propose Mind-to-Mind (Mind2), an ES framework that approaches interpretable ES context modeling for the ES dialogue generation task from a discourse analysis perspective. Specifically, we perform cognitive discourse analysis on ES dialogues according to our dynamic discourse context propagation window, which accommodates evolving context as the conversation between the ES system and user progresses. To enhance interpretability, Mind2 prioritizes details that reflect each speaker's belief about the other speaker with bidirectionality, integrating Theory-of-Mind, physiological expected utility, and cognitive rationality to extract cognitive knowledge from ES conversations. Experimental results support that Mind2 achieves competitive performance versus state-of-the-art ES systems while trained with only 10\% of the available training data.
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