How do different tokenizers perform on downstream tasks in scriptio
  continua languages?: A case study in Japanese
Takuro Fujii Koki Shibata Atsuki Yamaguchi Terufumi Morishita Yasuhiro Sogawa
This paper investigates the effect of tokenizers on the downstream
performance of pretrained language models (PLMs) in scriptio continua languages
where no explicit spaces exist between words, using Japanese as a case study.
The tokenizer for such languages often consists of a morphological analyzer and
a subword tokenizer, requiring us to conduct a comprehensive study of all
possible pairs. However, previous studies lack this comprehensiveness. We
therefore train extensive sets of tokenizers, build a PLM using each, and
measure the downstream performance on a wide range of tasks. Our results
demonstrate that each downstream task has a different optimal morphological
analyzer, and that it is better to use Byte-Pair-Encoding or Unigram rather
than WordPiece as a subword tokenizer, regardless of the type of task.