@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://preview.aclanthology.org/manual-author-scripts/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."
}