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)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.391.pdf
Export citation
@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
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.