提出可泛化论证挖掘框架，提升说服性文本结构分析能力
Unveiling Global Discourse Structures: Theoretical Analysis and NLP Applications in Argument Mining
- 构建面向全局论述结构的论证挖掘架构，增强模型泛化性
- 识别现有方法在论点抽取与分类中的不足，提出改进路径
- 适合研究自然语言推理、文本生成与批判性思维系统的学者
在全球话语结构中，连贯性在人类文本理解中起关键作用，是高质量文本的标志，尤其在说服性文本中，连贯的论证结构能有效支撑主张。本文探讨并提出检测、提取与表征这类全局话语结构的方法，该过程称为论证挖掘（Argument(ation) Mining）。首先定义话语结构分析的关键术语与流程，继而总结现有研究进展，并指出现有论点组件抽取与分类方法的不足。此外，本文提出一种新的论证挖掘架构，通过运用新颖的自然语言处理技术，提升模型泛化能力，克服当前研究挑战。本文综述现有知识，总结近期成果，并提出我们的NLP处理流水线，旨在推进对全局话语结构的理论理解。
原文摘要 · Abstract (English)
Particularly in the structure of global discourse, coherence plays a pivotal role in human text comprehension and is a hallmark of high-quality text. This is especially true for persuasive texts, where coherent argument structures support claims effectively. This paper discusses and proposes methods for detecting, extracting and representing these global discourse structures in a proccess called Argument(ation) Mining. We begin by defining key terms and processes of discourse structure analysis, then continue to summarize existing research on the matter, and identify shortcomings in current argument component extraction and classification methods. Furthermore, we will outline an architecture for argument mining that focuses on making models more generalisable while overcoming challenges in the current field of research by utilizing novel NLP techniques. This paper reviews current knowledge, summarizes recent works, and outlines our NLP pipeline, aiming to contribute to the theoretical understanding of global discourse structures.
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