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Leading sign language models lose over 80% of their BLEU score the moment they encounter an unseen signer, exposing widespread benchmark inflation driven by identity and sentence leakage.
Domain adaptation can dramatically enhance sign language recognition, achieving superior results over conventional transfer learning methods.
