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        <title>NoahNMT at WMT 2021: Dual Transfer for Very Low Resource Supervised Machine Translation</title>
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        <namePart type="given">Meng</namePart>
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    <name type="personal">
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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
%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.