Towards Comprehensive Argument Analysis in Education: Dataset, Tasks, and Method

2025-05-17

AI Summary

Existing argument mining approaches in educational settings predominantly rely on simplistic binary relations, failing to capture the intricate vertical (hierarchical) and horizontal (sequential/cohesive) argumentative structures prevalent in student essays. Method: We propose a fine-grained argumentation analysis framework tailored for education, introducing the first taxonomy of 14 argument relation types—including vertical relations (e.g., claim-support, evidence-explanation) and horizontal relations (e.g., contrast, concession, adversative shift). The framework jointly addresses argument unit identification, argument relation classification, and automated essay scoring via a unified architecture integrating large language model fine-tuning with multi-task learning, thereby co-modeling argument structure, writing quality, and discourse relations. Contribution/Results: Experiments demonstrate substantial improvements in argument unit detection and relation classification accuracy. Fine-grained relational annotation enhances both the interpretability and precision of automated scoring, advancing argument mining from binary modeling toward multidimensional, structured representation.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Knowledge Representation and Reasoning: Argumentation
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Web Mining and Content Analysis: Bridging structured and unstructured data
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Enhancing argument analysis with fine-grained relation types
• Improving detection of complex argument structures in education
• Exploring writing quality impact on argument component detection

Innovation

• Proposed 14 fine-grained argument relation types
• Conducted experiments on three key argument tasks
• Explored writing quality impact on argument analysis