Assessing the Importance of Frequency versus Compositionality for Subword-based Tokenization in NMT
Autor: | Wolleb, Benoist, Silvestri, Romain, Vernikos, Giorgos, Dolamic, Ljiljana, Popescu-Belis, Andrei |
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Rok vydání: | 2023 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | 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. Comment: Accepted at EAMT 2023 |
Databáze: | arXiv |
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