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        <title>CUNI-ÚFAL at the WMT26 Terminology Translation Task</title>
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        <namePart type="given">Aleš</namePart>
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            <title>Proceedings of the Eleventh Conference on Machine Translation</title>
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    <abstract>This paper describes the CUNI-ÚFAL submission to the WMT26 Terminology Translation Task. We present a model-agnostic pipeline that selects document-relevant terminology for a general-purpose LLM, instantiated with GPT-5.6-Sol for both tracks. For Track1 (explicit dictionary) we compare glossary placement, inline annotation, dictionary filtering, and terminology-aware revision: filtering the dictionary per document improves judged coverage and, slightly, translation quality. Inline term insertion buys a small coverage gain at the cost of fluency. For Track2 (in-domain seed bitexts) we compare presenting them as translation history, extracting a dictionary and reusing the Track1 pipeline, and their hybrid. Lacking development references, we evaluated with surface- and lemma-based term matching plus an LLM judge. Among submitted systems in the official evaluation, our Track1 submission ties for the highest COMET-22 score and obtains the best XCOMET-XXL and MetricX-24 XL scores, and our primary Track2 submission achieves the highest term success.</abstract>
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%0 Conference Proceedings
%T CUNI-ÚFAL at the WMT26 Terminology Translation Task
%A Papáček, Aleš Manuel
%A Sourada, Tomáš
%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 papacek-sourada-2026-cuni
%X This paper describes the CUNI-ÚFAL submission to the WMT26 Terminology Translation Task. We present a model-agnostic pipeline that selects document-relevant terminology for a general-purpose LLM, instantiated with GPT-5.6-Sol for both tracks. For Track1 (explicit dictionary) we compare glossary placement, inline annotation, dictionary filtering, and terminology-aware revision: filtering the dictionary per document improves judged coverage and, slightly, translation quality. Inline term insertion buys a small coverage gain at the cost of fluency. For Track2 (in-domain seed bitexts) we compare presenting them as translation history, extracting a dictionary and reusing the Track1 pipeline, and their hybrid. Lacking development references, we evaluated with surface- and lemma-based term matching plus an LLM judge. Among submitted systems in the official evaluation, our Track1 submission ties for the highest COMET-22 score and obtains the best XCOMET-XXL and MetricX-24 XL scores, and our primary Track2 submission achieves the highest term success.
%U https://aclanthology.org/2026.wmt-main.120/
%P 1849-1859

Markdown (Informal)

[CUNI-ÚFAL at the WMT26 Terminology Translation Task](https://aclanthology.org/2026.wmt-main.120/) (Papáček & Sourada, WMT 2026)

• CUNI-ÚFAL at the WMT26 Terminology Translation Task (Papáček & Sourada, WMT 2026)

ACL

• Aleš Manuel Papáček and Tomáš Sourada. 2026. CUNI-ÚFAL at the WMT26 Terminology Translation Task. In Proceedings of the Eleventh Conference on Machine Translation, pages 1849–1859, Budapest, Hungary. Association for Computational Linguistics.