FACET at WMT 2026 Automated Translation Quality Evaluation Task

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-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.

Computation and Language (cs.CL)

arXiv:2610.00096 [cs.CL]

(or arXiv:2610.00096v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.00096 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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