Transfer Learning of Argument Mining in Student Essays

Yuning Ding, Julian Lohmann, Nils-Jonathan Schaller, Thorben Jansen, Andrea Horbach

Abstract
This paper explores the transferability of a cross-prompt argument mining model trained on argumentative essays authored by native English-speaking learners (EN-L1) across educational contexts and languages. Specifically, the adaptability of a multilingual transformer model is assessed through its application to comparable argumentative essays authored by English-as-a-foreign-language learners (EN-L2) for context transfer, and a dataset composed of essays written by native German learners (DE) for both language and task transfer. To separate language effects from educational context effects, we also perform experiments on a machine-translated version of the German dataset (DE-MT). Our findings demonstrate that, even under zero-shot conditions, a model trained on native English speakers exhibits satisfactory performance on the EN-L2/DE datasets. Machine translation does not substantially enhance this performance, suggesting that distinct writing styles across educational contexts impact performance more than language differences.
Anthology ID:
2024.bea-1.36
Volume:
Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Ekaterina Kochmar, Marie Bexte, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Anaïs Tack, Victoria Yaneva, Zheng Yuan
Venue:
BEA
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
439–449
Language:
URL:
https://aclanthology.org/2024.bea-1.36
DOI:
Bibkey:
Cite (ACL):
Yuning Ding, Julian Lohmann, Nils-Jonathan Schaller, Thorben Jansen, and Andrea Horbach. 2024. Transfer Learning of Argument Mining in Student Essays. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pages 439–449, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Transfer Learning of Argument Mining in Student Essays (Ding et al., BEA 2024)
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