To Optimize, or Not to Optimize, That Is the Question: TelU-KU Models for WMT21 Large-Scale Multilingual Machine Translation
Sari Dewi Budiwati, Tirana Fatyanosa, Mahendra Data, Dedy Rahman Wijaya, Patrick Adolf Telnoni, Arie Ardiyanti Suryani, Agus Pratondo, Masayoshi Aritsugi
Correct Metadata for
Abstract
We describe TelU-KU models of large-scale multilingual machine translation for five Southeast Asian languages: Javanese, Indonesian, Malay, Tagalog, Tamil, and English. We explore a variation of hyperparameters of flores101_mm100_175M model using random search with 10% of datasets to improve BLEU scores of all thirty language pairs. We submitted two models, TelU-KU-175M and TelU-KU- 175M_HPO, with average BLEU scores of 12.46 and 13.19, respectively. Our models show improvement in most language pairs after optimizing the hyperparameters. We also identified three language pairs that obtained a BLEU score of more than 15 while using less than 70 sentences of the training dataset: Indonesian-Tagalog, Tagalog-Indonesian, and Malay-Tagalog.- Anthology ID:
- 2021.wmt-1.47
- Volume:
- Proceedings of the Sixth Conference on Machine Translation
- Month:
- November
- Year:
- 2021
- Address:
- Online
- Editors:
- Loic Barrault, Ondrej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussa, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Tom Kocmi, Andre Martins, Makoto Morishita, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 387–397
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.47
- DOI:
- Bibkey:
- Cite (ACL):
- Sari Dewi Budiwati, Tirana Fatyanosa, Mahendra Data, Dedy Rahman Wijaya, Patrick Adolf Telnoni, Arie Ardiyanti Suryani, Agus Pratondo, and Masayoshi Aritsugi. 2021. To Optimize, or Not to Optimize, That Is the Question: TelU-KU Models for WMT21 Large-Scale Multilingual Machine Translation. In Proceedings of the Sixth Conference on Machine Translation, pages 387–397, Online. Association for Computational Linguistics.
- Cite (Informal):
- To Optimize, or Not to Optimize, That Is the Question: TelU-KU Models for WMT21 Large-Scale Multilingual Machine Translation (Budiwati et al., WMT 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.wmt-1.47.pdf
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@inproceedings{budiwati-etal-2021-optimize,
title = "To Optimize, or Not to Optimize, That Is the Question: {T}el{U}-{KU} Models for {WMT}21 Large-Scale Multilingual Machine Translation",
author = "Budiwati, Sari Dewi and
Fatyanosa, Tirana and
Data, Mahendra and
Wijaya, Dedy Rahman and
Telnoni, Patrick Adolf and
Suryani, Arie Ardiyanti and
Pratondo, Agus and
Aritsugi, Masayoshi",
editor = "Barrault, Loic and
Bojar, Ondrej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-jussa, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kocmi, Tom and
Martins, Andre and
Morishita, Makoto and
Monz, Christof",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wmt-1.47",
pages = "387--397",
abstract = "We describe TelU-KU models of large-scale multilingual machine translation for five Southeast Asian languages: Javanese, Indonesian, Malay, Tagalog, Tamil, and English. We explore a variation of hyperparameters of flores101{\_}mm100{\_}175M model using random search with 10{\%} of datasets to improve BLEU scores of all thirty language pairs. We submitted two models, TelU-KU-175M and TelU-KU- 175M{\_}HPO, with average BLEU scores of 12.46 and 13.19, respectively. Our models show improvement in most language pairs after optimizing the hyperparameters. We also identified three language pairs that obtained a BLEU score of more than 15 while using less than 70 sentences of the training dataset: Indonesian-Tagalog, Tagalog-Indonesian, and Malay-Tagalog.",
}
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%0 Conference Proceedings %T To Optimize, or Not to Optimize, That Is the Question: TelU-KU Models for WMT21 Large-Scale Multilingual Machine Translation %A Budiwati, Sari Dewi %A Fatyanosa, Tirana %A Data, Mahendra %A Wijaya, Dedy Rahman %A Telnoni, Patrick Adolf %A Suryani, Arie Ardiyanti %A Pratondo, Agus %A Aritsugi, Masayoshi %Y Barrault, Loic %Y Bojar, Ondrej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussa, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Freitag, Markus %Y Graham, Yvette %Y Grundkiewicz, Roman %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Kocmi, Tom %Y Martins, Andre %Y Morishita, Makoto %Y Monz, Christof %S Proceedings of the Sixth Conference on Machine Translation %D 2021 %8 November %I Association for Computational Linguistics %C Online %F budiwati-etal-2021-optimize %X We describe TelU-KU models of large-scale multilingual machine translation for five Southeast Asian languages: Javanese, Indonesian, Malay, Tagalog, Tamil, and English. We explore a variation of hyperparameters of flores101_mm100_175M model using random search with 10% of datasets to improve BLEU scores of all thirty language pairs. We submitted two models, TelU-KU-175M and TelU-KU- 175M_HPO, with average BLEU scores of 12.46 and 13.19, respectively. Our models show improvement in most language pairs after optimizing the hyperparameters. We also identified three language pairs that obtained a BLEU score of more than 15 while using less than 70 sentences of the training dataset: Indonesian-Tagalog, Tagalog-Indonesian, and Malay-Tagalog. %U https://aclanthology.org/2021.wmt-1.47 %P 387-397
Markdown (Informal)
[To Optimize, or Not to Optimize, That Is the Question: TelU-KU Models for WMT21 Large-Scale Multilingual Machine Translation](https://aclanthology.org/2021.wmt-1.47) (Budiwati et al., WMT 2021)
- To Optimize, or Not to Optimize, That Is the Question: TelU-KU Models for WMT21 Large-Scale Multilingual Machine Translation (Budiwati et al., WMT 2021)
ACL
- Sari Dewi Budiwati, Tirana Fatyanosa, Mahendra Data, Dedy Rahman Wijaya, Patrick Adolf Telnoni, Arie Ardiyanti Suryani, Agus Pratondo, and Masayoshi Aritsugi. 2021. To Optimize, or Not to Optimize, That Is the Question: TelU-KU Models for WMT21 Large-Scale Multilingual Machine Translation. In Proceedings of the Sixth Conference on Machine Translation, pages 387–397, Online. Association for Computational Linguistics.