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            <title>Proceedings of the 17th International Conference on Spoken Language Translation</title>
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    <abstract>This paper describes Octanove Labs’ submission to the IWSLT 2020 open domain translation challenge. In order to build a high-quality Japanese-Chinese neural machine translation (NMT) system, we use a combination of 1) parallel corpus filtering and 2) back-translation. We have shown that, by using heuristic rules and learned classifiers, the size of the parallel data can be reduced by 70% to 90% without much impact on the final MT performance. We have also shown that including the artificially generated parallel data through back-translation further boosts the metric by 17% to 27%, while self-training contributes little. Aside from a small number of parallel sentences annotated for filtering, no external resources have been used to build our system.</abstract>
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%0 Conference Proceedings
%T Octanove Labs’ Japanese-Chinese Open Domain Translation System
%A Hagiwara, Masato
%Y Federico, Marcello
%Y Waibel, Alex
%Y Knight, Kevin
%Y Nakamura, Satoshi
%Y Ney, Hermann
%Y Niehues, Jan
%Y Stüker, Sebastian
%Y Wu, Dekai
%Y Mariani, Joseph
%Y Yvon, Francois
%S Proceedings of the 17th International Conference on Spoken Language Translation
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F hagiwara-2020-octanove
%X This paper describes Octanove Labs’ submission to the IWSLT 2020 open domain translation challenge. In order to build a high-quality Japanese-Chinese neural machine translation (NMT) system, we use a combination of 1) parallel corpus filtering and 2) back-translation. We have shown that, by using heuristic rules and learned classifiers, the size of the parallel data can be reduced by 70% to 90% without much impact on the final MT performance. We have also shown that including the artificially generated parallel data through back-translation further boosts the metric by 17% to 27%, while self-training contributes little. Aside from a small number of parallel sentences annotated for filtering, no external resources have been used to build our system.
%R 10.18653/v1/2020.iwslt-1.20
%U https://aclanthology.org/2020.iwslt-1.20
%U https://doi.org/10.18653/v1/2020.iwslt-1.20
%P 166-171

Markdown (Informal)

[Octanove Labs’ Japanese-Chinese Open Domain Translation System](https://aclanthology.org/2020.iwslt-1.20) (Hagiwara, IWSLT 2020)

• Octanove Labs’ Japanese-Chinese Open Domain Translation System (Hagiwara, IWSLT 2020)

ACL

• Masato Hagiwara. 2020. Octanove Labs’ Japanese-Chinese Open Domain Translation System. In Proceedings of the 17th International Conference on Spoken Language Translation, pages 166–171, Online. Association for Computational Linguistics.