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        <title>FACET at WMT 2026 Automated Translation Quality Evaluation Task</title>
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            <title>Proceedings of the Eleventh Conference on Machine Translation</title>
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    <abstract>Different error types in machine translation require different evidence. Whether meaning is preserved can be judged only against the source, while whether the target is well-formed, or whether it names one entity consistently, can be judged from the target alone. We present FACET, our reference-free submission to the WMT26 Automated Translation Quality Evaluation Task, which decomposes evaluation into Fluency, Accuracy, and Consistency passes and gives each pass only the context its error type requires. A single fixed model is prompted three times, and the merged error spans yield the three task outputs, error spans, quality scores, and error-free labels, with no trained components. We also submit FACET\ensuremath-C, which omits the Consistency pass. Without gold labels, we characterize the predictions of FACET. Its system rankings place post-edited human translation first, and the Consistency pass changes about a tenth of segment scores while leaving the ranking nearly unchanged.</abstract>
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%0 Conference Proceedings
%T FACET at WMT 2026 Automated Translation Quality Evaluation Task
%A Kim, Ahrii
%A Park, Chanjun
%A Kim, Seong-Heum
%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 kim-etal-2026-facet
%X Different error types in machine translation require different evidence. Whether meaning is preserved can be judged only against the source, while whether the target is well-formed, or whether it names one entity consistently, can be judged from the target alone. We present FACET, our reference-free submission to the WMT26 Automated Translation Quality Evaluation Task, which decomposes evaluation into Fluency, Accuracy, and Consistency passes and gives each pass only the context its error type requires. A single fixed model is prompted three times, and the merged error spans yield the three task outputs, error spans, quality scores, and error-free labels, with no trained components. We also submit FACET\ensuremath-C, which omits the Consistency pass. Without gold labels, we characterize the predictions of FACET. Its system rankings place post-edited human translation first, and the Consistency pass changes about a tenth of segment scores while leaving the ranking nearly unchanged.
%U https://aclanthology.org/2026.wmt-main.100/
%P 1667-1677

Markdown (Informal)

[FACET at WMT 2026 Automated Translation Quality Evaluation Task](https://aclanthology.org/2026.wmt-main.100/) (Kim et al., WMT 2026)

• FACET at WMT 2026 Automated Translation Quality Evaluation Task (Kim et al., WMT 2026)

ACL

• Ahrii Kim, Chanjun Park, and Seong-Heum Kim. 2026. FACET at WMT 2026 Automated Translation Quality Evaluation Task. In Proceedings of the Eleventh Conference on Machine Translation, pages 1667–1677, Budapest, Hungary. Association for Computational Linguistics.