CEAMC: Corpus and Empirical Study of Argument Analysis in Education via LLMs
Abstract: This paper introduces the Chinese Essay Argument Mining Corpus (CEAMC), a comprehensive dataset for fine-grained argument analysis. Existing argument types in education remain simplistic and isolated, failing to encapsulate complete argument information. Originating from authentic examination settings, CEAMC transcends previous simple representations by conducting multi-level delineation of argument components, thus capturing the subtle nuances of argumentation in the real world and meeting the needs of complex and diverse argumentative scenarios. Our contributions include the development of the CEAMC, the establishment of baselines for further research, and an in-depth exploration of the performance of Large Language Models (LLMs) on CEAMC. The results indicate that our CEAMC can serve as a challenging benchmark for the development of argument analysis in the field of education.
Paper Type: Long
Research Area: Semantics: Lexical and Sentence-Level
Research Area Keywords: argument mining
Contribution Types: Data resources, Data analysis
Languages Studied: English, Chinese
Submission Number: 3715
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