RWS at WMT26 Terminology Translation Task

Jon Cambra Guinea; Andrea Alfieri

RWS at WMT26 Terminology Translation Task

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Abstract

This submission is a prompt-based terminology and segment injection strategy for terminology translation across domains. We evaluate the technique on Basque to Spanish, English to Polish, and Traditional Chinese to English for WMT 2026 Terminology Shared Task 1 & 2. We demonstrate that injecting bilingual terminology and bi-text examples into recent Large Language Models’ (LLM) prompts improves both overall translation quality and terminology accuracy. Our results show that strong instruction-following ability allows a system to adapt to terminology and style constraints without the need for fine-tuning.

Anthology ID:: 2026.wmt-main.112
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:: 1772–1778
Language:
URL:: https://aclanthology.org/2026.wmt-main.112/
DOI:
Bibkey:
Cite (ACL):: Jon Cambra Guinea and Andrea Alfieri. 2026. RWS at WMT26 Terminology Translation Task. In Proceedings of the Eleventh Conference on Machine Translation, pages 1772–1778, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: RWS at WMT26 Terminology Translation Task (Cambra Guinea & Alfieri, WMT 2026)
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PDF:: https://aclanthology.org/2026.wmt-main.112.pdf

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@inproceedings{cambra-guinea-alfieri-2026-rws,
    title = "{RWS} at {WMT}26 Terminology Translation Task",
    author = "Cambra Guinea, Jon  and
      Alfieri, Andrea",
    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.112/",
    pages = "1772--1778",
    ISBN = "979-8-89176-474-3",
    abstract = "This submission is a prompt-based terminology and segment injection strategy for terminology translation across domains. We evaluate the technique on Basque to Spanish, English to Polish, and Traditional Chinese to English for WMT 2026 Terminology Shared Task 1 {\&} 2. We demonstrate that injecting bilingual terminology and bi-text examples into recent Large Language Models' (LLM) prompts improves both overall translation quality and terminology accuracy. Our results show that strong instruction-following ability allows a system to adapt to terminology and style constraints without the need for fine-tuning."
}

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    <abstract>This submission is a prompt-based terminology and segment injection strategy for terminology translation across domains. We evaluate the technique on Basque to Spanish, English to Polish, and Traditional Chinese to English for WMT 2026 Terminology Shared Task 1 &amp; 2. We demonstrate that injecting bilingual terminology and bi-text examples into recent Large Language Models’ (LLM) prompts improves both overall translation quality and terminology accuracy. Our results show that strong instruction-following ability allows a system to adapt to terminology and style constraints without the need for fine-tuning.</abstract>
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%0 Conference Proceedings
%T RWS at WMT26 Terminology Translation Task
%A Cambra Guinea, Jon
%A Alfieri, Andrea
%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 cambra-guinea-alfieri-2026-rws
%X This submission is a prompt-based terminology and segment injection strategy for terminology translation across domains. We evaluate the technique on Basque to Spanish, English to Polish, and Traditional Chinese to English for WMT 2026 Terminology Shared Task 1 & 2. We demonstrate that injecting bilingual terminology and bi-text examples into recent Large Language Models’ (LLM) prompts improves both overall translation quality and terminology accuracy. Our results show that strong instruction-following ability allows a system to adapt to terminology and style constraints without the need for fine-tuning.
%U https://aclanthology.org/2026.wmt-main.112/
%P 1772-1778

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Markdown (Informal)

[RWS at WMT26 Terminology Translation Task](https://aclanthology.org/2026.wmt-main.112/) (Cambra Guinea & Alfieri, WMT 2026)

RWS at WMT26 Terminology Translation Task (Cambra Guinea & Alfieri, WMT 2026)

ACL

Jon Cambra Guinea and Andrea Alfieri. 2026. RWS at WMT26 Terminology Translation Task. In Proceedings of the Eleventh Conference on Machine Translation, pages 1772–1778, Budapest, Hungary. Association for Computational Linguistics.