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arXiv · 2306.01393

Assessing the Importance of Frequency versus Compositionality for Subword-based Tokenization in NMT

Benoist Wolleb·Romain Silvestri·Giorgos Vernikos·Ljiljana Dolamic·Andrei Popescu-Belis
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Abstract

Subword tokenization is the de facto standard for tokenization in neural language models and machine translation systems. Three advantages are frequently cited in favor of subwords: shorter encoding of frequent tokens, compositionality of subwords, and ability to deal with unknown words. As their relative importance is not entirely clear yet, we propose a tokenization approach that enables us to separate frequency (the first advantage) from compositionality. The approach uses Huffman coding to tokenize words, by order of frequency, using a fixed amount of symbols. Experiments with CS-DE, EN-FR and EN-DE NMT show that frequency alone accounts for 90%-95% of the scores reached by BPE, hence compositionality has less importance than previously thought.

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Benoist Wolleb, Romain Silvestri, Giorgos Vernikos, Ljiljana Dolamic, Andrei Popescu-Belis. 2023-06-02. Assessing the Importance of Frequency versus Compositionality for Subword-based Tokenization in NMT. https://arxiv.org/abs/2306.01393

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