Document detail
ID
oai:arXiv.org:2404.00397
Topic
Computer Science - Computation and...Author
Cognetta, Marco Hiraoka, Tatsuya Okazaki, Naoaki Sennrich, Rico Pinter, YuvalCategory
Computer Science
Year
2024
listing date
4/3/2024
Keywords
vocabulary trimming subwordsAbstract
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
A Novel MR Imaging Sequence of 3D-ZOOMit Real Inversion-Recovery Imaging Improves Endolymphatic Hydrops Detection in Patients with Ménière Disease
ménière disease p < detection imaging sequences 3d-zoomit 3d endolymphatic real tse reconstruction ir inversion-recovery hydrops ratio
