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University of Edinburgh
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Machine translation benchmarks have functionally saturated, but pairing human-authored failure cases with deterministic verification rules reveals critical multimodal blind spots that automated metrics consistently miss.
MT models show a troubling bias towards US content, with specialized models proving less robust across diverse locales.