When the Student Becomes the Master: Learning Better and Smaller Monolingual Models from mBERT

Pranaydeep Singh; Els Lefever

When the Student Becomes the Master: Learning Better and Smaller Monolingual Models from mBERT

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Abstract

In this research, we present pilot experiments to distil monolingual models from a jointly trained model for 102 languages (mBERT). We demonstrate that it is possible for the target language to outperform the original model, even with a basic distillation setup. We evaluate our methodology for 6 languages with varying amounts of resources and language families.

Anthology ID:: 2022.coling-1.391
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:: 4434–4441
Language:
URL:: https://aclanthology.org/2022.coling-1.391/
DOI:
Bibkey:
Cite (ACL):: Pranaydeep Singh and Els Lefever. 2022. When the Student Becomes the Master: Learning Better and Smaller Monolingual Models from mBERT. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4434–4441, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):: When the Student Becomes the Master: Learning Better and Smaller Monolingual Models from mBERT (Singh & Lefever, COLING 2022)
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PDF:: https://aclanthology.org/2022.coling-1.391.pdf

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@inproceedings{singh-lefever-2022-student,
    title = "When the Student Becomes the Master: Learning Better and Smaller Monolingual Models from m{BERT}",
    author = "Singh, Pranaydeep  and
      Lefever, Els",
    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-1.391/",
    pages = "4434--4441",
    abstract = "In this research, we present pilot experiments to distil monolingual models from a jointly trained model for 102 languages (mBERT). We demonstrate that it is possible for the target language to outperform the original model, even with a basic distillation setup. We evaluate our methodology for 6 languages with varying amounts of resources and language families."
}

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%0 Conference Proceedings
%T When the Student Becomes the Master: Learning Better and Smaller Monolingual Models from mBERT
%A Singh, Pranaydeep
%A Lefever, Els
%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 singh-lefever-2022-student
%X In this research, we present pilot experiments to distil monolingual models from a jointly trained model for 102 languages (mBERT). We demonstrate that it is possible for the target language to outperform the original model, even with a basic distillation setup. We evaluate our methodology for 6 languages with varying amounts of resources and language families.
%U https://aclanthology.org/2022.coling-1.391/
%P 4434-4441

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Markdown (Informal)

[When the Student Becomes the Master: Learning Better and Smaller Monolingual Models from mBERT](https://aclanthology.org/2022.coling-1.391/) (Singh & Lefever, COLING 2022)

When the Student Becomes the Master: Learning Better and Smaller Monolingual Models from mBERT (Singh & Lefever, COLING 2022)

ACL

Pranaydeep Singh and Els Lefever. 2022. When the Student Becomes the Master: Learning Better and Smaller Monolingual Models from mBERT. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4434–4441, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.