构建对话数据集，提升学生心理辅导系统实用性
ConvCounsel: A Conversational Dataset for Student Counseling
- 聚焦心理咨询中的主动倾听策略，构建多模态对话数据集
- 包含语音与文本数据，支持真实场景下的对话系统开发
- 适合研究心理健康对话系统、人机交互的学者与开发者
学生心理健康问题亟需关注，但多数高校咨询师与学生的比例超过推荐标准250:1，导致面询等待时间过长，影响治疗效果。现有公开心理相关数据集或话题泛化，或因伦理限制难以直接应用。本文提出专用于心理辅导对话的多模态数据集ConvCounsel，强调主动倾听策略，包含语音与文本数据，支持可靠对话系统开发。为验证其有效性，本文还基于该数据集构建了口语化心理对话系统NYCUKA。实验结果表明，该数据集对提升系统实用性具有显著价值。
原文摘要 · Abstract (English)
Student mental health is a sensitive issue that necessitates special attention. A primary concern is the student-to-counselor ratio, which surpasses the recommended standard of 250:1 in most universities. This imbalance results in extended waiting periods for in-person consultations, which cause suboptimal treatment. Significant efforts have been directed toward developing mental health dialogue systems utilizing the existing open-source mental health-related datasets. However, currently available datasets either discuss general topics or various strategies that may not be viable for direct application due to numerous ethical constraints inherent in this research domain. To address this issue, this paper introduces a specialized mental health dataset that emphasizes the active listening strategy employed in conversation for counseling, also named as ConvCounsel. This dataset comprises both speech and text data, which can facilitate the development of a reliable pipeline for mental health dialogue systems. To demonstrate the utility of the proposed dataset, this paper also presents the NYCUKA, a spoken mental health dialogue system that is designed by using the ConvCounsel dataset. The results show the merit of using this dataset.
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