@inproceedings{fujii-etal-2023-different,
title = "How do different tokenizers perform on downstream tasks in scriptio continua languages?: A case study in {J}apanese",
author = "Fujii, Takuro and
Shibata, Koki and
Yamaguchi, Atsuki and
Morishita, Terufumi and
Sogawa, Yasuhiro",
editor = "Padmakumar, Vishakh and
Vallejo, Gisela and
Fu, Yao",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-srw.5",
doi = "10.18653/v1/2023.acl-srw.5",
pages = "39--49",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T How do different tokenizers perform on downstream tasks in scriptio continua languages?: A case study in Japanese
%A Fujii, Takuro
%A Shibata, Koki
%A Yamaguchi, Atsuki
%A Morishita, Terufumi
%A Sogawa, Yasuhiro
%Y Padmakumar, Vishakh
%Y Vallejo, Gisela
%Y Fu, Yao
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F fujii-etal-2023-different
%X 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.
%R 10.18653/v1/2023.acl-srw.5
%U https://aclanthology.org/2023.acl-srw.5
%U https://doi.org/10.18653/v1/2023.acl-srw.5
%P 39-49
Markdown (Informal)
[How do different tokenizers perform on downstream tasks in scriptio continua languages?: A case study in Japanese](https://aclanthology.org/2023.acl-srw.5) (Fujii et al., ACL 2023)
ACL