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LocQE: Principled Domain Adaptation for Localisation Quality Estimation by Leveraging Post-Edits

arXiv · AI, language, vision and robotics · article · Sep 16, 2026 · UTC

Learned quality estimation (QE) models such as COMETKiwi are widespread and work well for general machine translation evaluation. However, they are known to struggle on unseen domains, limiting their performance in a real-world localisation context. We show that they are insensitive to some important factors in localisation, such as whether numbers are translated accurately, or even whether the correct number of spaces and punctuation are preserved in a translation. Further, a key capability for optimisation of machine translation is the ability of QE models to accurately rank different transl

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