 Models using this encoding also generalize better to non-standard dialects without explicit training . We suggest metrics to evaluate tokenizers and extensive model-independent analyses demonstrate the efficacy of the encoding when used together with data-driven subword tokenizers  .
<meaning-changed>  Models using this encoding also generalize better to non-standard dialects without explicit training . We suggest metrics to evaluate tokenizers and extensive model-independent analyses demonstrate the efficacy of the encoding when used together with data-driven subword tokenizers  .
 Models using this encoding also generalize better to non-standard dialects without explicit training and translation models converge faster when trained with BITE. Finally, we show that our encoding improves the vocabulary efficiency of popular data-driven subword tokenizers .