发表机构
Fondazione Bruno Kessler; Universidade da Coruña; IEGPS-CSIC, Spanish National Research Council（布鲁诺·凯斯勒基金会; 科鲁尼亚大学; 西班牙国家研究委员会IEGPS）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过三重视角框架系统分析端到端论证挖掘流水线，提出通用设计以促进方法比较与评估，为未来研究奠定基础。
AI 中文摘要
论证挖掘（AM）将自然语言转换为其潜在的论证结构。这种转换通常通过一系列AM任务来实现，这些任务构成一个端到端的AM流水线。然而，AM方法在如何概念化这些任务上往往存在差异，使得它们之间的直接比较变得困难且不透明。这要求对AM方法进行更细致、任务层面的分析，以实现更清晰的比较和评估。本工作提出了一项初步的元研究，系统性地回顾了几项最先进的端到端AM工作，并通过一个三重视角框架——语言学视角、计算视角和领域视角——分析其流水线，以理解流水线如何将论证建模为结构、如何计算这些结构以及如何整合领域知识。我们进一步提出了针对语言学和计算视角的通用设计，说明了关键AM任务如何为论证结构的建模和计算而设计。我们提出的框架为跨AM方法的方法论中心描述奠定了基础，有助于未来研究中更深入的理解和更系统的比较。
英文摘要
Argument Mining (AM) transforms natural language into its underlying argument structures. This transformation is typically realized through a sequence of AM tasks that form an end-to-end AM pipeline. However, AM approaches often differ in how they conceptualize these tasks, making direct comparisons between them difficult and opaque. This calls for a more nuanced, task-level analysis of AM approaches to enable clearer comparison and assessment. This work presents a preliminary meta-study that systematically reviews several state-of-the-art end-to-end AM works and analyzes their pipelines through a triple-perspective framework---a linguistic, computational and domain perspective---to understand how the pipelines model arguments as structures, computes them, and integrates domain knowledge. We further propose a general design to the linguistic and computational perspectives, illustrating how key AM tasks are designed for modeling and computation of argument structures. Our proposed framework lays the groundwork for methodology-centered descriptions across AM approaches, facilitating deeper understanding and more systematic comparisons in future research.
Comments12 pages, 3 figures, European Conference on Argumentation 2025 (ECA 2025)
