ReMova: Fine-tuning LLMs for English to Belarusian translation

Mikita Pilinka; Aliaksandr Kliuyeu; David Samuel; Yves Scherrer

ReMova: Fine-tuning LLMs for English to Belarusian translation

Mikita Pilinka, Aliaksandr Kliuyeu, David Samuel, Yves Scherrer

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Abstract

This paper presents a Belarusian-specific data-cleaning pipeline and fine-tuning for English-Belarusian machine translation. Our cleaning pipeline distinguishes itself from others by employing a correction tool that addresses the issue of the two orthographies of the Belarusian language, noise in the training data, interference from other languages, and other misspelling issues common in Belarusian on the internet. A matched ablation on unfiltered training data shows substantial benefits from filtering for all fine-tuned models, with the LLM-based models gaining roughly twice as much from filtering as the dedicated encoder-decoder MT system, supporting the view that for Belarusian MT one of the primary bottlenecks is data quality.

Anthology ID:: 2026.wmt-main.75
Volume:: Proceedings of the Eleventh Conference on Machine Translation
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Barry Haddow, Tom Kocmi, Philipp Koehn, Christof Monz
Venues:: WMT | WS
SIG:: SIGMT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 1378–1390
Language:
URL:: https://aclanthology.org/2026.wmt-main.75/
DOI:
Bibkey:
Cite (ACL):: Mikita Pilinka, Aliaksandr Kliuyeu, David Samuel, and Yves Scherrer. 2026. ReMova: Fine-tuning LLMs for English to Belarusian translation. In Proceedings of the Eleventh Conference on Machine Translation, pages 1378–1390, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: ReMova: Fine-tuning LLMs for English to Belarusian translation (Pilinka et al., WMT 2026)
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PDF:: https://aclanthology.org/2026.wmt-main.75.pdf

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@inproceedings{pilinka-etal-2026-remova,
    title = "{R}e{M}ova: Fine-tuning {LLM}s for {E}nglish to {B}elarusian translation",
    author = "Pilinka, Mikita  and
      Kliuyeu, Aliaksandr  and
      Samuel, David  and
      Scherrer, Yves",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Eleventh Conference on Machine Translation",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.wmt-main.75/",
    pages = "1378--1390",
    ISBN = "979-8-89176-474-3",
    abstract = "This paper presents a Belarusian-specific data-cleaning pipeline and fine-tuning for English-Belarusian machine translation. Our cleaning pipeline distinguishes itself from others by employing a correction tool that addresses the issue of the two orthographies of the Belarusian language, noise in the training data, interference from other languages, and other misspelling issues common in Belarusian on the internet. A matched ablation on unfiltered training data shows substantial benefits from filtering for all fine-tuned models, with the LLM-based models gaining roughly twice as much from filtering as the dedicated encoder-decoder MT system, supporting the view that for Belarusian MT one of the primary bottlenecks is data quality."
}

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%0 Conference Proceedings
%T ReMova: Fine-tuning LLMs for English to Belarusian translation
%A Pilinka, Mikita
%A Kliuyeu, Aliaksandr
%A Samuel, David
%A Scherrer, Yves
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F pilinka-etal-2026-remova
%X This paper presents a Belarusian-specific data-cleaning pipeline and fine-tuning for English-Belarusian machine translation. Our cleaning pipeline distinguishes itself from others by employing a correction tool that addresses the issue of the two orthographies of the Belarusian language, noise in the training data, interference from other languages, and other misspelling issues common in Belarusian on the internet. A matched ablation on unfiltered training data shows substantial benefits from filtering for all fine-tuned models, with the LLM-based models gaining roughly twice as much from filtering as the dedicated encoder-decoder MT system, supporting the view that for Belarusian MT one of the primary bottlenecks is data quality.
%U https://aclanthology.org/2026.wmt-main.75/
%P 1378-1390

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Markdown (Informal)

[ReMova: Fine-tuning LLMs for English to Belarusian translation](https://aclanthology.org/2026.wmt-main.75/) (Pilinka et al., WMT 2026)

ReMova: Fine-tuning LLMs for English to Belarusian translation (Pilinka et al., WMT 2026)

ACL

Mikita Pilinka, Aliaksandr Kliuyeu, David Samuel, and Yves Scherrer. 2026. ReMova: Fine-tuning LLMs for English to Belarusian translation. In Proceedings of the Eleventh Conference on Machine Translation, pages 1378–1390, Budapest, Hungary. Association for Computational Linguistics.