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Adding language-specific cues in tokenization can significantly improve how multilingual models handle cross-lingual homographs, leading to better translation outcomes.
Subword tokenization just got a whole lot more efficient: ToaST slashes token counts by 11% and boosts language model performance by up to 7.6% compared to standard methods.
