发表机构
Gachon University; Yonsei University（嘉泉大学; 延世大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有认知扭曲检测方法忽略扭曲思维心理结构的问题，提出MTI-GNN模型，在多数据集上验证其性能优于基线模型及生成模型，且各视角均有效。
AI 中文摘要
认知扭曲检测是计算精神卫生领域的关键任务，但现有方法常忽略扭曲思维的心理结构。本文提出MTI-GNN（多视角三元组交互图神经网络），将贝克认知三元组——对自我、世界和未来的负面看法——建模为互补的分类视角。大语言模型（LLM）将每段话语分解为三个视角，据此构建视角特定的相似度图并由多视角GNN编码；三元组交互模块通过顺序源条件更新和逐特征门控建模跨视角依赖，原型引导的视角融合执行标签条件聚合；标签扩展监督在训练时纳入所有可用扭曲标注。在涵盖10种扭曲类别的4个韩语、英语、中文数据集的9764个样本上评估MTI-GNN，其在零样本和少样本设置下显著优于所有监督变体及8个提示式生成模型；留一视角消融实验显示三个视角均有显著贡献，人类专家评估也初步证明其与预期认知维度的一致性。
英文摘要
Cognitive distortion detection is a key task in computational mental health, yet existing approaches often overlook the psychological structure of distorted thoughts. We propose MTI-GNN (Multi-Perspective Triad Interaction Graph Neural Network), which models Beck's cognitive triad---negative views of the self, world, and future---as complementary perspectives for classification. An LLM decomposes each utterance into the three perspectives, from which perspective-specific similarity graphs are constructed and encoded by a Multi-Perspective GNN. A Triad Interaction module models cross-perspective dependencies through sequential source-conditioned updates and feature-wise gating, while Prototype-Guided Perspective Fusion performs label-conditioned aggregation. Label-expanded supervision incorporates all available distortion annotations during training. We evaluate MTI-GNN on 9,764 samples from four Korean, English, and Chinese datasets spanning ten distortion categories. MTI-GNN significantly outperforms all supervised variants and exceeds eight prompted generative models under zero-shot and few-shot settings. Leave-one-perspective-out ablations show that all three perspectives contribute significantly, while human expert evaluation provides preliminary evidence of their alignment with the intended cognitive dimensions.
