MaxMatch-Dropout: Subword Regularization for WordPiece

Tatsuya Hiraoka

MaxMatch-Dropout: Subword Regularization for WordPiece

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Abstract

We present a subword regularization method for WordPiece, which uses a maximum matching algorithm for tokenization. The proposed method, MaxMatch-Dropout, randomly drops words in a search using the maximum matching algorithm. It realizes finetuning with subword regularization for popular pretrained language models such as BERT-base. The experimental results demonstrate that MaxMatch-Dropout improves the performance of text classification and machine translation tasks as well as other subword regularization methods. Moreover, we provide a comparative analysis of subword regularization methods: subword regularization with SentencePiece (Unigram), BPE-Dropout, and MaxMatch-Dropout.

Anthology ID:: 2022.coling-1.430
Volume:: Proceedings of the 29th International Conference on Computational Linguistics
Month:: October
Year:: 2022
Address:: Gyeongju, Republic of Korea
Editors:: Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
Venue:: COLING
SIG:
Publisher:: International Committee on Computational Linguistics
Note:
Pages:: 4864–4872
Language:
URL:: https://aclanthology.org/2022.coling-1.430/
DOI:
Bibkey:
Cite (ACL):: Tatsuya Hiraoka. 2022. MaxMatch-Dropout: Subword Regularization for WordPiece. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4864–4872, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):: MaxMatch-Dropout: Subword Regularization for WordPiece (Hiraoka, COLING 2022)
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PDF:: https://aclanthology.org/2022.coling-1.430.pdf

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@inproceedings{hiraoka-2022-maxmatch,
    title = "{M}ax{M}atch-Dropout: Subword Regularization for {W}ord{P}iece",
    author = "Hiraoka, Tatsuya",
    editor = "Calzolari, Nicoletta  and
      Huang, Chu-Ren  and
      Kim, Hansaem  and
      Pustejovsky, James  and
      Wanner, Leo  and
      Choi, Key-Sun  and
      Ryu, Pum-Mo  and
      Chen, Hsin-Hsi  and
      Donatelli, Lucia  and
      Ji, Heng  and
      Kurohashi, Sadao  and
      Paggio, Patrizia  and
      Xue, Nianwen  and
      Kim, Seokhwan  and
      Hahm, Younggyun  and
      He, Zhong  and
      Lee, Tony Kyungil  and
      Santus, Enrico  and
      Bond, Francis  and
      Na, Seung-Hoon",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.430/",
    pages = "4864--4872",
    abstract = "We present a subword regularization method for WordPiece, which uses a maximum matching algorithm for tokenization. The proposed method, MaxMatch-Dropout, randomly drops words in a search using the maximum matching algorithm. It realizes finetuning with subword regularization for popular pretrained language models such as BERT-base. The experimental results demonstrate that MaxMatch-Dropout improves the performance of text classification and machine translation tasks as well as other subword regularization methods. Moreover, we provide a comparative analysis of subword regularization methods: subword regularization with SentencePiece (Unigram), BPE-Dropout, and MaxMatch-Dropout."
}

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%0 Conference Proceedings
%T MaxMatch-Dropout: Subword Regularization for WordPiece
%A Hiraoka, Tatsuya
%Y Calzolari, Nicoletta
%Y Huang, Chu-Ren
%Y Kim, Hansaem
%Y Pustejovsky, James
%Y Wanner, Leo
%Y Choi, Key-Sun
%Y Ryu, Pum-Mo
%Y Chen, Hsin-Hsi
%Y Donatelli, Lucia
%Y Ji, Heng
%Y Kurohashi, Sadao
%Y Paggio, Patrizia
%Y Xue, Nianwen
%Y Kim, Seokhwan
%Y Hahm, Younggyun
%Y He, Zhong
%Y Lee, Tony Kyungil
%Y Santus, Enrico
%Y Bond, Francis
%Y Na, Seung-Hoon
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F hiraoka-2022-maxmatch
%X We present a subword regularization method for WordPiece, which uses a maximum matching algorithm for tokenization. The proposed method, MaxMatch-Dropout, randomly drops words in a search using the maximum matching algorithm. It realizes finetuning with subword regularization for popular pretrained language models such as BERT-base. The experimental results demonstrate that MaxMatch-Dropout improves the performance of text classification and machine translation tasks as well as other subword regularization methods. Moreover, we provide a comparative analysis of subword regularization methods: subword regularization with SentencePiece (Unigram), BPE-Dropout, and MaxMatch-Dropout.
%U https://aclanthology.org/2022.coling-1.430/
%P 4864-4872

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Markdown (Informal)

[MaxMatch-Dropout: Subword Regularization for WordPiece](https://aclanthology.org/2022.coling-1.430/) (Hiraoka, COLING 2022)

MaxMatch-Dropout: Subword Regularization for WordPiece (Hiraoka, COLING 2022)

ACL

Tatsuya Hiraoka. 2022. MaxMatch-Dropout: Subword Regularization for WordPiece. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4864–4872, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.