Papers with vocabulary and entropy
Self-Vocabularizing Training for Neural Machine Translation (2025.naacl-srw)
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| Challenge: | Past vocabulary learning techniques identify relevant vocabulary before training, relying on corpus statistics or frequency counts without considering contextual information or the model's ability to represent it. |
| Approach: | They propose a method that self-vocabularizes a smaller, more optimal vocabulary by pairing source sentences with the model's predictions to define a new vocabulary. |
| Outcome: | The proposed method produces a 1.49 BLEU improvement in the simulated model and an increase in unique token usage and a 6–8% reduction in vocabulary size. |