<b>Discourse Relation Dataset on Hands-on Engineering Tutorial Monologue Video Scripts Based on PDTB3 Scheme</b>

My research focuses on developing a specialized discourse relation dataset from hands-on engineering tutorial monologue video transcripts, based on the Penn Discourse Treebank 3.0 (PDTB-3) annotation framework. The core objective of this work is to systematically analyze and annotate how knowledge is structured and conveyed in technical instructional videos, which are a growing resource for self-guided learning in engineering domains.The study involves collecting transcripts from real-world engineering tutorials such as those on plumbing, welding, electrical wiring, and HVAC systems and annotating them with discourse relations that capture the logical, temporal, and causal connections between instructional steps. Using the PDTB-3 taxonomy, I categorized these relations into senses such as Contingency.Cause, Temporal.Synchronous, Expansion.Instantiation, and Comparison.Contrast, among others.What sets this research apart is its domain specificity and its incorporation of challenging discourse phenomena, including implicit connectives, non-adjacent argument spans, alternative lexicalizations (AltLex), and compound relations. These features reflect the nuanced ways expert instructors communicate procedural knowledge. To support this, I implemented a two-stage annotation process first, AI-assisted segmentation , followed by manual refinement and inter-annotator agreement evaluation.Additionally, the dataset was used to benchmark the discourse annotation capabilities of several Large Language Models (LLMs), such as ChatGPT-4, Claude 3 Sonnet, Gemini 2.0, and Grok 3.0. Metrics like accuracy and F1-score were used to compare their performance against human annotations.Overall, the research aims to contribute a high-quality resource that can be used to improve automated discourse understanding in technical education contexts. It bridges gaps in existing discourse datasets by targeting domain-specific, spoken-form, procedural knowledge—making it highly relevant for applications in AI-based tutoring systems, expert knowledge transfer, and educational content analysis.本研究以宾夕法尼亚话语树库3.0（Penn Discourse Treebank 3.0，PDTB-3）标注框架为基础，从实操型工程教学独白视频的转录文本中构建专属话语关系数据集。本研究的核心目标是系统性分析并标注工程领域技术教学视频中知识的组织与传递方式——这类视频目前已成为工程领域自主学习的日益重要的资源。本研究收集现实场景中的工程教学转录文本，涵盖管道工程、焊接作业、电气布线以及供热通风与空气调节（Heating, Ventilation and Air Conditioning，HVAC）系统等教程，并基于话语关系对其进行标注，以捕捉教学步骤间的逻辑、时序与因果关联。依托PDTB-3的分类体系，本研究将这些话语关系划分为若干语义类别，包括Contingency.Cause（偶然关系·因果）、Temporal.Synchronous（时序关系·同步）、Expansion.Instantiation（扩展关系·实例化）以及Comparison.Contrast（比较关系·对比）等。本研究的独特之处在于其领域针对性，以及对多种复杂话语现象的覆盖——包括隐性连接词、非相邻论元跨度、替代词汇化（Alternative Lexicalizations，AltLex）以及复合话语关系等。这些特征精准体现了专业讲师传递程序性知识时的细腻沟通逻辑。为此，本研究采用两阶段标注流程：首先开展AI辅助的文本分段，随后进行人工校验与标注者间一致性评估。此外，本数据集被用于对多款大语言模型（Large Language Models，LLMs）的话语标注能力开展基准测试，涉及ChatGPT-4、Claude 3 Sonnet、Gemini 2.0以及Grok 3.0等模型。研究采用准确率、F1值等指标，将这些模型的标注表现与人工标注结果进行对比。总体而言，本研究旨在构建一项高质量资源，用于提升技术教育场景下的自动化话语理解能力。本数据集针对领域专属的口语化程序性知识进行构建，弥补了现有话语数据集的空白，因此在AI辅助教学系统、专家知识迁移以及教育内容分析等领域均具有极高的应用价值。