No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models

Agnes Luhtaru, Elizaveta Korotkova, Mark Fishel


Abstract
Grammatical Error Correction (GEC) enhances language proficiency and promotes effective communication, but research has primarily centered around English. We propose a simple approach to multilingual and low-resource GEC by exploring the potential of multilingual machine translation (MT) models for error correction. We show that MT models are not only capable of error correction out-of-the-box, but that they can also be fine-tuned to even better correction quality. Results show the effectiveness of this approach, with our multilingual model outperforming similar-sized mT5-based models and even competing favourably with larger models.
Anthology ID:
2024.eacl-long.73
Volume:
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
March
Year:
2024
Address:
St. Julian’s, Malta
Editors:
Yvette Graham, Matthew Purver
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1209–1222
Language:
URL:
https://aclanthology.org/2024.eacl-long.73
DOI:
Bibkey:
Cite (ACL):
Agnes Luhtaru, Elizaveta Korotkova, and Mark Fishel. 2024. No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1209–1222, St. Julian’s, Malta. Association for Computational Linguistics.
Cite (Informal):
No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models (Luhtaru et al., EACL 2024)
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PDF:
https://aclanthology.org/2024.eacl-long.73.pdf
Video:
 https://aclanthology.org/2024.eacl-long.73.mp4