NoahNMT at WMT 2021: Dual Transfer for Very Low Resource Supervised Machine Translation

Meng Zhang; Minghao Wu; Pengfei Li; Liangyou Li; Qun Liu

NoahNMT at WMT 2021: Dual Transfer for Very Low Resource Supervised Machine Translation

Meng Zhang, Minghao Wu, Pengfei Li, Liangyou Li, Qun Liu

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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.

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)
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PDF:: https://aclanthology.org/2021.wmt-1.108.pdf

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@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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%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

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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.