将辅导对话按问题分割并关联资料，提升教学结构分析效率
Problem-Oriented Segmentation and Retrieval: Case Study on Tutoring Conversations
- 联合建模对话分割与资料检索，统一处理问题导向的对话结构
- 在3500段真实辅导对话上，联合方法比独立流程高76%准确率
- 适用于教育研究、智能辅导系统，尤其关注教学时间与语言模式
许多开放式对话（如辅导课程或商务会议）围绕预定义参考资料展开。为研究此类对话结构，我们提出问题导向分割与检索（POSR）任务：联合将对话切分为片段，并将每个片段关联到相关参考内容。以教育场景为例，我们构建了首个真实辅导课程数据集LessonLink，包含3500个对话段落，覆盖24,300分钟教学内容，对应116道SAT数学题。我们评估了多种联合与独立方法，包括TextTiling、ColBERT及大语言模型。结果表明，将POSR视为联合任务至关重要：其在联合指标上较独立流水线最高提升76%，在分割指标上比传统方法最高提升78%。实验还揭示了真实教学中语言表达与时间分配的新规律，展示了该方法在下游教育应用中的实用价值。
原文摘要 · Abstract (English)
Many open-ended conversations (e.g., tutoring lessons or business meetings) revolve around pre-defined reference materials, like worksheets or meeting bullets. To provide a framework for studying such conversation structure, we introduce Problem-Oriented Segmentation & Retrieval (POSR), the task of jointly breaking down conversations into segments and linking each segment to the relevant reference item. As a case study, we apply POSR to education where effectively structuring lessons around problems is critical yet difficult. We present LessonLink, the first dataset of real-world tutoring lessons, featuring 3,500 segments, spanning 24,300 minutes of instruction and linked to 116 SAT math problems. We define and evaluate several joint and independent approaches for POSR, including segmentation (e.g., TextTiling), retrieval (e.g., ColBERT), and large language models (LLMs) methods. Our results highlight that modeling POSR as one joint task is essential: POSR methods outperform independent segmentation and retrieval pipelines by up to +76% on joint metrics and surpass traditional segmentation methods by up to +78% on segmentation metrics. We demonstrate POSR's practical impact on downstream education applications, deriving new insights on the language and time use in real-world lesson structures.
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