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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.
Angular dispersion regularization not only alleviates representation collapse in NMT Transformers, but also boosts translation quality and maintains its benefits post-quantization.
