Abstract
This paper describes the NoahNMT system submitted to the WMT 2021 shared task of Very Low Resource Supervised Machine Translation. The system is a standard Transformer model equipped with our recent technique of dual transfer. It also employs widely used techniques that are known to be helpful for neural machine translation, including iterative back-translation, selected finetuning, and ensemble. The final submission achieves the top BLEU for three translation directions.- Anthology ID:
- 2021.wmt-1.108
- 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:
- 1009–1013
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.108
- DOI:
- Bibkey:
- Cite (ACL):
- Meng Zhang, Minghao Wu, Pengfei Li, Liangyou Li, and Qun Liu. 2021. NoahNMT at WMT 2021: Dual Transfer for Very Low Resource Supervised Machine Translation. In Proceedings of the Sixth Conference on Machine Translation, pages 1009–1013, Online. Association for Computational Linguistics.
- Cite (Informal):
- NoahNMT at WMT 2021: Dual Transfer for Very Low Resource Supervised Machine Translation (Zhang et al., WMT 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.wmt-1.108.pdf
Export citation
@inproceedings{zhang-etal-2021-noahnmt,
title = "{N}oah{NMT} at {WMT} 2021: Dual Transfer for Very Low Resource Supervised Machine Translation",
author = "Zhang, Meng and
Wu, Minghao and
Li, Pengfei and
Li, Liangyou and
Liu, Qun",
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-1.108",
pages = "1009--1013",
abstract = "This paper describes the NoahNMT system submitted to the WMT 2021 shared task of Very Low Resource Supervised Machine Translation. The system is a standard Transformer model equipped with our recent technique of dual transfer. It also employs widely used techniques that are known to be helpful for neural machine translation, including iterative back-translation, selected finetuning, and ensemble. The final submission achieves the top BLEU for three translation directions.",
}
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<abstract>This paper describes the NoahNMT system submitted to the WMT 2021 shared task of Very Low Resource Supervised Machine Translation. The system is a standard Transformer model equipped with our recent technique of dual transfer. It also employs widely used techniques that are known to be helpful for neural machine translation, including iterative back-translation, selected finetuning, and ensemble. The final submission achieves the top BLEU for three translation directions.</abstract>
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%0 Conference Proceedings %T NoahNMT at WMT 2021: Dual Transfer for Very Low Resource Supervised Machine Translation %A Zhang, Meng %A Wu, Minghao %A Li, Pengfei %A Li, Liangyou %A Liu, Qun %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 zhang-etal-2021-noahnmt %X This paper describes the NoahNMT system submitted to the WMT 2021 shared task of Very Low Resource Supervised Machine Translation. The system is a standard Transformer model equipped with our recent technique of dual transfer. It also employs widely used techniques that are known to be helpful for neural machine translation, including iterative back-translation, selected finetuning, and ensemble. The final submission achieves the top BLEU for three translation directions. %U https://aclanthology.org/2021.wmt-1.108 %P 1009-1013
Markdown (Informal)
[NoahNMT at WMT 2021: Dual Transfer for Very Low Resource Supervised Machine Translation](https://aclanthology.org/2021.wmt-1.108) (Zhang et al., WMT 2021)
- NoahNMT at WMT 2021: Dual Transfer for Very Low Resource Supervised Machine Translation (Zhang et al., WMT 2021)
ACL
- Meng Zhang, Minghao Wu, Pengfei Li, Liangyou Li, and Qun Liu. 2021. NoahNMT at WMT 2021: Dual Transfer for Very Low Resource Supervised Machine Translation. In Proceedings of the Sixth Conference on Machine Translation, pages 1009–1013, Online. Association for Computational Linguistics.