Machine Translation for Low Resource Turkic Languages: English-Tatar

Alexander Dikov

Machine Translation for Low Resource Turkic Languages: English-Tatar

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Abstract

This paper outlines our winning submission to the English-to-Tatar translation task. We evaluated three strategies: few-shot prompting with Gemini 3 Pro Preview, specialized trans-tokenized Tweeties models, and the RL-distilled TranslateGemma family. Results demonstrate that large commercial models significantly outperform smaller specialized ones in this low-resource setting. Gemini secured first place with a chrF++ score of 56.71, surpassing the open-source baseline of 25.23.

Anthology ID:: 2026.loresmt-1.21
Volume:: Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026)
Month:: March
Year:: 2026
Address:: Rabat, Morocco
Editors:: Atul Kr. Ojha, Chao-hong Liu, Ekaterina Vylomova, Flammie Pirinen, Jonathan Washington, Nathaniel Oco, Xiaobing Zhao
Venues:: LoResMT | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 222–224
Language:
URL:: https://aclanthology.org/2026.loresmt-1.21/
DOI:
Bibkey:
Cite (ACL):: Alexander Dikov. 2026. Machine Translation for Low Resource Turkic Languages: English-Tatar. In Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026), pages 222–224, Rabat, Morocco. Association for Computational Linguistics.
Cite (Informal):: Machine Translation for Low Resource Turkic Languages: English-Tatar (Dikov, LoResMT 2026)
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PDF:: https://aclanthology.org/2026.loresmt-1.21.pdf

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@inproceedings{dikov-2026-machine,
    title = "Machine Translation for Low Resource {T}urkic Languages: {E}nglish-{T}atar",
    author = "Dikov, Alexander",
    editor = "Ojha, Atul Kr.  and
      Liu, Chao-hong  and
      Vylomova, Ekaterina  and
      Pirinen, Flammie  and
      Washington, Jonathan  and
      Oco, Nathaniel  and
      Zhao, Xiaobing",
    booktitle = "Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages ({L}o{R}es{MT} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.loresmt-1.21/",
    pages = "222--224",
    ISBN = "979-8-89176-366-1",
    abstract = "This paper outlines our winning submission to the English-to-Tatar translation task. We evaluated three strategies: few-shot prompting with Gemini 3 Pro Preview, specialized trans-tokenized Tweeties models, and the RL-distilled TranslateGemma family. Results demonstrate that large commercial models significantly outperform smaller specialized ones in this low-resource setting. Gemini secured first place with a chrF++ score of 56.71, surpassing the open-source baseline of 25.23."
}

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            <namePart type="given">Xiaobing</namePart>
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    <abstract>This paper outlines our winning submission to the English-to-Tatar translation task. We evaluated three strategies: few-shot prompting with Gemini 3 Pro Preview, specialized trans-tokenized Tweeties models, and the RL-distilled TranslateGemma family. Results demonstrate that large commercial models significantly outperform smaller specialized ones in this low-resource setting. Gemini secured first place with a chrF++ score of 56.71, surpassing the open-source baseline of 25.23.</abstract>
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%0 Conference Proceedings
%T Machine Translation for Low Resource Turkic Languages: English-Tatar
%A Dikov, Alexander
%Y Ojha, Atul Kr.
%Y Liu, Chao-hong
%Y Vylomova, Ekaterina
%Y Pirinen, Flammie
%Y Washington, Jonathan
%Y Oco, Nathaniel
%Y Zhao, Xiaobing
%S Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-366-1
%F dikov-2026-machine
%X This paper outlines our winning submission to the English-to-Tatar translation task. We evaluated three strategies: few-shot prompting with Gemini 3 Pro Preview, specialized trans-tokenized Tweeties models, and the RL-distilled TranslateGemma family. Results demonstrate that large commercial models significantly outperform smaller specialized ones in this low-resource setting. Gemini secured first place with a chrF++ score of 56.71, surpassing the open-source baseline of 25.23.
%U https://aclanthology.org/2026.loresmt-1.21/
%P 222-224

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Markdown (Informal)

[Machine Translation for Low Resource Turkic Languages: English-Tatar](https://aclanthology.org/2026.loresmt-1.21/) (Dikov, LoResMT 2026)

Machine Translation for Low Resource Turkic Languages: English-Tatar (Dikov, LoResMT 2026)

ACL

Alexander Dikov. 2026. Machine Translation for Low Resource Turkic Languages: English-Tatar. In Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026), pages 222–224, Rabat, Morocco. Association for Computational Linguistics.