Abstract
We present our development of the multilingual machine translation system for the large-scale multilingual machine translation task at WMT 2021. Starting form the provided baseline system, we investigated several techniques to improve the translation quality on the target subset of languages. We were able to significantly improve the translation quality by adapting the system towards the target subset of languages and by generating synthetic data using the initial model. Techniques successfully applied in zero-shot multilingual machine translation (e.g. similarity regularizer) only had a minor effect on the final translation performance.- Anthology ID:
- 2021.wmt-1.51
- 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:
- 425–430
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.51
- DOI:
- Bibkey:
- Cite (ACL):
- Danni Liu and Jan Niehues. 2021. Maastricht University’s Large-Scale Multilingual Machine Translation System for WMT 2021. In Proceedings of the Sixth Conference on Machine Translation, pages 425–430, Online. Association for Computational Linguistics.
- Cite (Informal):
- Maastricht University’s Large-Scale Multilingual Machine Translation System for WMT 2021 (Liu & Niehues, WMT 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.wmt-1.51.pdf
- Data
- FLoRes-101
Export citation
@inproceedings{liu-niehues-2021-maastricht-universitys,
title = "Maastricht University{'}s Large-Scale Multilingual Machine Translation System for {WMT} 2021",
author = "Liu, Danni and
Niehues, Jan",
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-1.51",
pages = "425--430",
abstract = "We present our development of the multilingual machine translation system for the large-scale multilingual machine translation task at WMT 2021. Starting form the provided baseline system, we investigated several techniques to improve the translation quality on the target subset of languages. We were able to significantly improve the translation quality by adapting the system towards the target subset of languages and by generating synthetic data using the initial model. Techniques successfully applied in zero-shot multilingual machine translation (e.g. similarity regularizer) only had a minor effect on the final translation performance.",
}
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%0 Conference Proceedings %T Maastricht University’s Large-Scale Multilingual Machine Translation System for WMT 2021 %A Liu, Danni %A Niehues, Jan %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 liu-niehues-2021-maastricht-universitys %X We present our development of the multilingual machine translation system for the large-scale multilingual machine translation task at WMT 2021. Starting form the provided baseline system, we investigated several techniques to improve the translation quality on the target subset of languages. We were able to significantly improve the translation quality by adapting the system towards the target subset of languages and by generating synthetic data using the initial model. Techniques successfully applied in zero-shot multilingual machine translation (e.g. similarity regularizer) only had a minor effect on the final translation performance. %U https://aclanthology.org/2021.wmt-1.51 %P 425-430
Markdown (Informal)
[Maastricht University’s Large-Scale Multilingual Machine Translation System for WMT 2021](https://aclanthology.org/2021.wmt-1.51) (Liu & Niehues, WMT 2021)
- Maastricht University’s Large-Scale Multilingual Machine Translation System for WMT 2021 (Liu & Niehues, WMT 2021)
ACL
- Danni Liu and Jan Niehues. 2021. Maastricht University’s Large-Scale Multilingual Machine Translation System for WMT 2021. In Proceedings of the Sixth Conference on Machine Translation, pages 425–430, Online. Association for Computational Linguistics.