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Idioms have long been a challenge for machine translation, and while large language models (LLMs) have proven increasingly adept at handling them, whether quality estimation (QE) systems have kept pace and can reliably score idiomatic renderings of these expressions above faulty literal ones remains unclear. We introdu...
Existing automatic metrics for evaluating terminological use in machine translation (MT) penalise any divergence from a fixed reference, conflating translation errors with the valid terminological variation that human translators routinely produce. We introduce TermJudge, a document-level terminology metric that assign...
It is argued for variation-aware evaluation that conditions consistency penalties on whether target-side variation mirrors source-side variation, and constraining MT with a glossary improves accuracy and consistency but degrades CTV by suppressing valid variation.
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