构建细粒度论点分析体系，助力教育场景下的写作质量评估
Towards Comprehensive Argument Analysis in Education: Dataset, Tasks, and Method
- 提出14种细粒度论点关系，覆盖垂直与水平维度
- 在论点识别、关系预测与作文评分任务中验证有效性
- 揭示写作风格与论点结构的深层关联，适合教育研究者
论点挖掘近年来受到广泛关注，大型语言模型的发展进一步推动了这一趋势。然而，当前的论点关系仍较简单基础，难以全面捕捉真实场景中复杂的论点结构信息。为此，本文从垂直和水平两个维度提出14种细粒度关系类型，以更全面地刻画论点组件间的复杂互动。在此基础上，我们在三个任务上进行了大量实验：论点组件检测、关系预测与自动作文评分。同时，探究了写作质量对论点组件检测与关系预测的影响，以及话语关系与论证特征之间的关联。结果表明，细粒度论点标注对论证性写作质量评估至关重要，并倡导多维度的论点分析。
原文摘要 · Abstract (English)
Argument mining has garnered increasing attention over the years, with the recent advancement of Large Language Models (LLMs) further propelling this trend. However, current argument relations remain relatively simplistic and foundational, struggling to capture the full scope of argument information, particularly when it comes to representing complex argument structures in real-world scenarios. To address this limitation, we propose 14 fine-grained relation types from both vertical and horizontal dimensions, thereby capturing the intricate interplay between argument components for a thorough understanding of argument structure. On this basis, we conducted extensive experiments on three tasks: argument component detection, relation prediction, and automated essay grading. Additionally, we explored the impact of writing quality on argument component detection and relation prediction, as well as the connections between discourse relations and argumentative features. The findings highlight the importance of fine-grained argumentative annotations for argumentative writing quality assessment and encourage multi-dimensional argument analysis.
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