<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="keita-etal-2026-grammatical">
    <titleInfo>
        <title>Grammatical Error Correction for Low-Resource Languages: The Case of Zarma</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Mamadou</namePart>
        <namePart type="given">K</namePart>
        <namePart type="family">Keita</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Marcos</namePart>
        <namePart type="family">Zampieri</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Christopher</namePart>
        <namePart type="given">M</namePart>
        <namePart type="family">Homan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Adwoa</namePart>
        <namePart type="given">Asantewaa</namePart>
        <namePart type="family">Bremang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Dennis</namePart>
        <namePart type="family">Asamoah Owusu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Huy</namePart>
        <namePart type="family">Le</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-03</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Hansi</namePart>
            <namePart type="family">Hettiarachchi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tharindu</namePart>
            <namePart type="family">Ranasinghe</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alistair</namePart>
            <namePart type="family">Plum</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Paul</namePart>
            <namePart type="family">Rayson</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mohamed</namePart>
            <namePart type="family">Gaber</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Damith</namePart>
            <namePart type="family">Premasiri</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Fiona</namePart>
            <namePart type="given">Anting</namePart>
            <namePart type="family">Tan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lasitha</namePart>
            <namePart type="family">Uyangodage</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Rabat, Morocco</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-377-7</identifier>
    </relatedItem>
    <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.</abstract>
    <identifier type="citekey">keita-etal-2026-grammatical</identifier>
    <location>
        <url>https://aclanthology.org/2026.loreslm-1.9/</url>
    </location>
    <part>
        <date>2026-03</date>
        <extent unit="page">
            <start>98</start>
            <end>109</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Grammatical Error Correction for Low-Resource Languages: The Case of Zarma
%A Keita, Mamadou K.
%A Zampieri, Marcos
%A Homan, Christopher M.
%A Bremang, Adwoa Asantewaa
%A Asamoah Owusu, Dennis
%A Le, Huy
%Y Hettiarachchi, Hansi
%Y Ranasinghe, Tharindu
%Y Plum, Alistair
%Y Rayson, Paul
%Y Mitkov, Ruslan
%Y Gaber, Mohamed
%Y Premasiri, Damith
%Y Tan, Fiona Anting
%Y Uyangodage, Lasitha
%S Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-377-7
%F keita-etal-2026-grammatical
%X 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.
%U https://aclanthology.org/2026.loreslm-1.9/
%P 98-109

Markdown (Informal)

[Grammatical Error Correction for Low-Resource Languages: The Case of Zarma](https://aclanthology.org/2026.loreslm-1.9/) (Keita et al., LoResLM 2026)

• Grammatical Error Correction for Low-Resource Languages: The Case of Zarma (Keita et al., LoResLM 2026)

ACL

• Mamadou K. Keita, Marcos Zampieri, Christopher M Homan, Adwoa Asantewaa Bremang, Dennis Asamoah Owusu, and Huy Le. 2026. Grammatical Error Correction for Low-Resource Languages: The Case of Zarma. In Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026), pages 98–109, Rabat, Morocco. Association for Computational Linguistics.