CEAMC: Corpus and Empirical Study of Argument Analysis in Education via LLMs
Yupei Ren, Hongyi Wu, Zhaoguang Long, Shangqing Zhao, Xinyi Zhou, Zheqin Yin, Xinlin Zhuang, Xiaopeng Bai, Man Lan
Abstract
This paper introduces the Chinese Essay Argument Mining Corpus (CEAMC), a manually annotated dataset designed for argument component classification on multiple levels of granularity. Existing argument component types in education remain simplistic and isolated, failing to encapsulate the complete argument information. Originating from authentic examination settings, CEAMC categorizes argument components into 4 coarse-grained and 10 fine-grained delineations, surpassing previous simple representations to capture the subtle nuances of argumentation in the real world, thus meeting the needs of complex and diverse argumentative scenarios. Our contributions include the development of CEAMC, the establishment of baselines for further research, and a thorough 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 education.- Anthology ID:
- 2024.findings-emnlp.408
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6949–6966
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2024.findings-emnlp.408/
- DOI:
- 10.18653/v1/2024.findings-emnlp.408
- Cite (ACL):
- Yupei Ren, Hongyi Wu, Zhaoguang Long, Shangqing Zhao, Xinyi Zhou, Zheqin Yin, Xinlin Zhuang, Xiaopeng Bai, and Man Lan. 2024. CEAMC: Corpus and Empirical Study of Argument Analysis in Education via LLMs. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 6949–6966, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- CEAMC: Corpus and Empirical Study of Argument Analysis in Education via LLMs (Ren et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2024.findings-emnlp.408.pdf