Leveraging Multilingual Models for Robust Grammatical Error Correction Across Low-Resource Languages
Divesh Ramesh Kubal, Apurva Shrikant Nagvenkar
Abstract
Grammatical Error Correction (GEC) is a crucial task in Natural Language Processing (NLP) aimed at improving the quality of user-generated content, particularly for non-native speakers. This paper introduces a novel end-to-end architecture utilizing the M2M100 multilingual transformer model to build a unified GEC system, with a focus on low-resource languages. A synthetic data generation pipeline is proposed, tailored to address language-specific error categories. The system has been implemented for the Spanish language, showing promising results based on evaluations conducted by linguists with expertise in Spanish. Additionally, we present a user analysis that tracks user interactions, revealing an acceptance rate of 88.2%, as reflected by the actions performed by users.
BibTeX
@inproceedings{kubal-nagvenkar-2025-leveraging,
title = "Leveraging Multilingual Models for Robust Grammatical Error Correction Across Low-Resource Languages",
author = "Kubal, Divesh Ramesh and
Nagvenkar, Apurva Shrikant",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven and
Darwish, Kareem and
Agarwal, Apoorv",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-industry.43/",
pages = "505--510"
}