Détail du document
Identifiant
oai:arXiv.org:2404.00397
Sujet
Computer Science - Computation and...Auteur
Cognetta, Marco Hiraoka, Tatsuya Okazaki, Naoaki Sennrich, Rico Pinter, YuvalCatégorie
Computer Science
Année
2024
Date de référencement
03/04/2024
Mots clés
vocabulary trimming subwordsRésumé
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
Investigating the Serotonergic Modulation of Orientation Tuning of Neurons in Primary Visual Cortex of Anesthetized Mice
cortical tuning mean serotonin firing modulation rate neurons orientation visual activity system
