构建首个面向在线心理辅导的细粒度对话分类数据集，支持精准分析咨询对话。
OnCoCo 1.0: A Public Dataset for Fine-Grained Message Classification in Online Counseling Conversations
- 设计38类咨询师与28类来访者话语标签体系，覆盖更细粒度互动类型。
- 构建含2800条标注消息的数据集，支持自动化分析心理辅导文本。
- 开源数据与模型，助力心理健康领域对话研究与应用开发。
本文提出OnCoCo 1.0，一个面向在线心理辅导对话的细粒度消息分类公开数据集。现有分类体系多基于动机访谈（MI），受限于窄范围视角及以面对面咨询为主的数据来源，难以细致分析文本化咨询内容。为此，我们设计了一套综合编码方案，区分38种咨询师发言与28种来访者发言类型，并构建了包含约2800条消息的标注数据集。我们在该数据集上微调多个模型以验证其适用性。数据与模型均对研究者和实践者公开。本工作为语言资源社区提供了新型细粒度对话资源，扩展了社会心理对话分析的数据基础。
原文摘要 · Abstract (English)
This paper presents OnCoCo 1.0, a new public dataset for fine-grained message classification in online counseling. It is based on a new, integrative system of categories, designed to improve the automated analysis of psychosocial online counseling conversations. Existing category systems, predominantly based on Motivational Interviewing (MI), are limited by their narrow focus and dependence on datasets derived mainly from face-to-face counseling. This limits the detailed examination of textual counseling conversations. In response, we developed a comprehensive new coding scheme that differentiates between 38 types of counselor and 28 types of client utterances, and created a labeled dataset consisting of about 2.800 messages from counseling conversations. We fine-tuned several models on our dataset to demonstrate its applicability. The data and models are publicly available to researchers and practitioners. Thus, our work contributes a new type of fine-grained conversational resource to the language resources community, extending existing datasets for social and mental-health dialogue analysis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。