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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)
- 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.