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    <abstract>Kyrgyz is a morphologically rich Turkic language that remains significantly underrepresented in modern multilingual language models. To address this resource gap, we introduce KyrText, a diverse, large-scale corpus containing 680.5 million words. Unlike existing web-crawled datasets which are often noisy or misidentified, KyrText aggregates high-quality news, Wikipedia entries, digitized literature, and extensive legal archives from the Supreme Court and Ministry of Justice of the Kyrgyz Republic. We leverage this corpus for the continual pre-training of mBERT, XLM-R, and DeBERTaV3, while also training RoBERTa architectures from scratch.Evaluations across several bench marks—including natural language inference (XNLI), question answering (BoolQ), sentiment analysis (SST-2), and paraphrase identification (PAWS-X)—demonstrate that targeted pre-training on KyrText yields substantial performance improvements over baseline multilingual models.Our findings indicate that while base-sized models benefit immediately from this domain-specific data, larger architectures require more extensive training cycles to fully realize their potential. We release our corpus and suite of models to establish a new foundation for Kyrgyz Natural Language Processing.</abstract>
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%0 Conference Proceedings
%T KyrText: A Multi-Domain Large-Scale Corpus for Kyrgyz Language
%A Chubakov, Tilek
%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 chubakov-2026-kyrtext
%X Kyrgyz is a morphologically rich Turkic language that remains significantly underrepresented in modern multilingual language models. To address this resource gap, we introduce KyrText, a diverse, large-scale corpus containing 680.5 million words. Unlike existing web-crawled datasets which are often noisy or misidentified, KyrText aggregates high-quality news, Wikipedia entries, digitized literature, and extensive legal archives from the Supreme Court and Ministry of Justice of the Kyrgyz Republic. We leverage this corpus for the continual pre-training of mBERT, XLM-R, and DeBERTaV3, while also training RoBERTa architectures from scratch.Evaluations across several bench marks—including natural language inference (XNLI), question answering (BoolQ), sentiment analysis (SST-2), and paraphrase identification (PAWS-X)—demonstrate that targeted pre-training on KyrText yields substantial performance improvements over baseline multilingual models.Our findings indicate that while base-sized models benefit immediately from this domain-specific data, larger architectures require more extensive training cycles to fully realize their potential. We release our corpus and suite of models to establish a new foundation for Kyrgyz Natural Language Processing.
%U https://aclanthology.org/2026.loreslm-1.39/
%P 453-460

Markdown (Informal)

[KyrText: A Multi-Domain Large-Scale Corpus for Kyrgyz Language](https://aclanthology.org/2026.loreslm-1.39/) (Chubakov, LoResLM 2026)

• KyrText: A Multi-Domain Large-Scale Corpus for Kyrgyz Language (Chubakov, LoResLM 2026)

ACL

• Tilek Chubakov. 2026. KyrText: A Multi-Domain Large-Scale Corpus for Kyrgyz Language. In Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026), pages 453–460, Rabat, Morocco. Association for Computational Linguistics.