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An Analysis of BPE Vocabulary Trimming in Neural Machine Translation
Mar 30, 2024
We explore threshold vocabulary trimming in Byte-Pair Encoding subword tokenization, a postprocessing step that replaces rare subwords with their component subwords. The technique is available in popular tokenization libraries but has not been subjected to rigorous scientific scrutiny. While the removal of rare subwords is suggested as best practice in machine translation implementations, both as a means to reduce model size and for improving model performance through robustness, our experiments indicate that, across a large space of hyperparameter settings, vocabulary trimming fails to improve performance, and is even prone to incurring heavy degradation.
Two Counterexamples to Tokenization and the Noiseless Channel
Feb 29, 2024In Tokenization and the Noiseless Channel (Zouhar et al., 2023a), R\'enyi efficiency is suggested as an intrinsic mechanism for evaluating a tokenizer: for NLP tasks, the tokenizer which leads to the highest R\'enyi efficiency of the unigram distribution should be chosen. The R\'enyi efficiency is thus treated as a predictor of downstream performance (e.g., predicting BLEU for a machine translation task), without the expensive step of training multiple models with different tokenizers. Although useful, the predictive power of this metric is not perfect, and the authors note there are additional qualities of a good tokenization scheme that R\'enyi efficiency alone cannot capture. We describe two variants of BPE tokenization which can arbitrarily increase R\'enyi efficiency while decreasing the downstream model performance. These counterexamples expose cases where R\'enyi efficiency fails as an intrinsic tokenization metric and thus give insight for building more accurate predictors.