Grammatical Error Correction for Low-Resource Languages: The Case of Zarma

• URL: http://arxiv.org/abs/2410.15539v2
• Date: Sun, 16 Feb 2025 15:41:58 GMT
• Title: Grammatical Error Correction for Low-Resource Languages: The Case of Zarma
• Authors: Mamadou K. Keita, Christopher Homan, Marcos Zampieri, Adwoa Bremang, Habibatou Abdoulaye Alfari, Elysabhete Amadou Ibrahim, Dennis Owusu,
• Abstract summary: Grammatical error correction aims to improve quality and readability of texts. We present a study on GEC for Zarma, spoken by over five million in West Africa. We compare three approaches: rule-based methods, machine translation (MT) models, and large language models.
• Score: 8.40484790921164
• License:
• Abstract: Grammatical error correction (GEC) aims to improve quality and readability of texts through accurate correction of linguistic mistakes. Previous work has focused on high-resource languages, while low-resource languages lack robust tools. However, low-resource languages often face problems such as: non-standard orthography, limited annotated corpora, and diverse dialects, which slows down the development of GEC tools. We present a study on GEC for Zarma, spoken by over five million in West Africa. We compare three approaches: rule-based methods, machine translation (MT) models, and large language models (LLMs). We evaluated them 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 -- 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.

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