@inproceedings{keita-etal-2026-grammatical,
    title = "Grammatical Error Correction for Low-Resource Languages: The Case of {Z}arma",
    author = "Keita, Mamadou K.  and
      Bremang, Adwoa  and
      Le, Huy  and
      Owusu, Dennis  and
      Zampieri, Marcos  and
      Homan, Christopher",
    editor = "Hettiarachchi, Hansi  and
      Ranasinghe, Tharindu  and
      Plum, Alistair  and
      Rayson, Paul  and
      Mitkov, Ruslan  and
      Gaber, Mohamed  and
      Premasiri, Damith  and
      Tan, Fiona Anting  and
      Uyangodage, Lasitha",
    booktitle = "Proceedings of the Second Workshop on Language Models for Low-Resource Languages ({L}o{R}es{LM} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/missing-isa-paper/2026.loreslm-1.9/",
    doi = "10.18653/v1/2026.loreslm-1.9",
    pages = "98--109",
    ISBN = "979-8-89176-377-7",
    abstract = "Grammatical error correction (GEC) aims to improve text quality and readability. Previous work on the task focused primarily on high-resource languages, while low-resource languages lack robust tools. To address this shortcoming, we present a study on GEC for Zarma, a language spoken by over five million people in West Africa. We compare three approaches: rule-based methods, machine translation (MT) models, and large language models (LLMs). We evaluated GEC models using a dataset of more than 250,000 examples, including synthetic and human-annotated data. Our results showed that the MT-based approach using M2M100 outperforms others, with a detection rate of 95.82{\%} and a suggestion accuracy of 78.90{\%} in automatic evaluations (AE) and an average score of 3.0 out of 5.0 in manual evaluation (ME) from native speakers for grammar and logical corrections. The rule-based method was effective for spelling errors but failed on complex context-level errors. LLMs{---}Gemma 2b and MT5-small{---}showed moderate performance. Our work supports use of MT models to enhance GEC in low-resource settings, and we validated these results with Bambara, another West African language."
}