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        <title>Beyond Multilinguality: Typological Limitations in Multilingual Models for Meitei Language</title>
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    <name type="personal">
        <namePart type="given">Badal</namePart>
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            <title>Proceedings of the 8th Workshop on Research in Computational Linguistic Typology and Multilingual NLP</title>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Vylomova</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Andrei</namePart>
            <namePart type="family">Shcherbakov</namePart>
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        <name type="personal">
            <namePart type="given">Priya</namePart>
            <namePart type="family">Rani</namePart>
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    <abstract>We present MeiteiRoBERTa, the first publicly available monolingual RoBERTa-based language model for Meitei (Manipuri), a low-resource language spoken by over 1.8 million people in Northeast India. Trained from scratch on 76 million words of Meitei text in Bengali script, our model achieves a perplexity of 65.89, representing a 5.2× improvement over multilingual baselines BERT (341.56) and MuRIL (355.65). Through comprehensive evaluation on perplexity, tokenization efficiency, and semantic representation quality, we demonstrate that domain-specific pre training significantly outperforms general-purpose multilingual models for low-resource languages. Our model exhibits superior semantic understanding with 0.769 similarity separation compared to 0.035 for mBERT and near-zero for MuRIL, despite MuRIL’s better tokenization efficiency (fertility: 3.29 vs. 4.65). We publicly release the model, training code, and datasets to accelerate NLP research for Meitei and other underrepresented Northeast Indian languages</abstract>
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%0 Conference Proceedings
%T Beyond Multilinguality: Typological Limitations in Multilingual Models for Meitei Language
%A Nyalang, Badal
%Y Vylomova, Ekaterina
%Y Shcherbakov, Andrei
%Y Rani, Priya
%S Proceedings of the 8th Workshop on Research in Computational Linguistic Typology and Multilingual NLP
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-374-6
%F nyalang-2026-beyond
%X We present MeiteiRoBERTa, the first publicly available monolingual RoBERTa-based language model for Meitei (Manipuri), a low-resource language spoken by over 1.8 million people in Northeast India. Trained from scratch on 76 million words of Meitei text in Bengali script, our model achieves a perplexity of 65.89, representing a 5.2× improvement over multilingual baselines BERT (341.56) and MuRIL (355.65). Through comprehensive evaluation on perplexity, tokenization efficiency, and semantic representation quality, we demonstrate that domain-specific pre training significantly outperforms general-purpose multilingual models for low-resource languages. Our model exhibits superior semantic understanding with 0.769 similarity separation compared to 0.035 for mBERT and near-zero for MuRIL, despite MuRIL’s better tokenization efficiency (fertility: 3.29 vs. 4.65). We publicly release the model, training code, and datasets to accelerate NLP research for Meitei and other underrepresented Northeast Indian languages
%U https://aclanthology.org/2026.sigtyp-main.5/
%P 32-38

Markdown (Informal)

[Beyond Multilinguality: Typological Limitations in Multilingual Models for Meitei Language](https://aclanthology.org/2026.sigtyp-main.5/) (Nyalang, SIGTYP 2026)

• Beyond Multilinguality: Typological Limitations in Multilingual Models for Meitei Language (Nyalang, SIGTYP 2026)

ACL

• Badal Nyalang. 2026. Beyond Multilinguality: Typological Limitations in Multilingual Models for Meitei Language. In Proceedings of the 8th Workshop on Research in Computational Linguistic Typology and Multilingual NLP, pages 32–38, Rabat, Morocco. Association for Computational Linguistics.