@inproceedings{metinov-2026-ensemble,
    title = "Ensemble Methods for Low-Resource {R}ussian-{K}yrgyz Machine Translation: When Diverse Models Beat Better Models",
    author = "Metinov, Adilet",
    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-1.24/",
    pages = "235--237",
    ISBN = "979-8-89176-366-1",
    abstract = "We present our submission to the LoResMT 2026 Shared Task on Russian-Kyrgyz machine translation. Our approach demonstrates that ensembling diverse translation models with simple consensus-based voting can significantly outperform individual models, achieving a +1.37 CHRF++ improvement over our best single model. Notably, we find that including ``weaker'' models in the ensemble improves overall performance, challenging the conventional assumption that ensembles should only combine top-performing systems. Our system achieved 49.31 CHRF++ on the public leaderboard and 48.55 CHRF++ on the final private test set, placing 3rd in the Russian-Kyrgyz track using only open-weight models without any fine-tuning on parallel Kyrgyz data. We report several counter-intuitive findings: (1) simple voting outperforms quality-weighted selection, (2) more diverse models help even when individually weaker, and (3) post-processing ``corrections'' can hurt performance when reference translations contain similar artifacts."
}