Dokumentdetails
ID
oai:arXiv.org:2404.00397
Thema
Computer Science - Computation and...Autor
Cognetta, Marco Hiraoka, Tatsuya Okazaki, Naoaki Sennrich, Rico Pinter, YuvalKategorie
Computer Science
Jahr
2024
Auflistungsdatum
03.04.2024
Schlüsselwörter
vocabulary trimming subwordsZusammenfassung
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.
;Comment: 15 pages
Cognetta, Marco,Hiraoka, Tatsuya,Okazaki, Naoaki,Sennrich, Rico,Pinter, Yuval, 2024, An Analysis of BPE Vocabulary Trimming in Neural Machine Translation
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