  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.
