@inproceedings{ng-etal-2019-facebook,
title = "{F}acebook {FAIR}{'}s {WMT}19 News Translation Task Submission",
author = "Ng, Nathan and
Yee, Kyra and
Baevski, Alexei and
Ott, Myle and
Auli, Michael and
Edunov, Sergey",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5333",
doi = "10.18653/v1/W19-5333",
pages = "314--319",
abstract = "This paper describes Facebook FAIR{'}s submission to the WMT19 shared news translation task. We participate in four language directions, English {\textless}-{\textgreater} German and English {\textless}-{\textgreater} Russian in both directions. Following our submission from last year, our baseline systems are large BPE-based transformer models trained with the FAIRSEQ sequence modeling toolkit. This year we experiment with different bitext data filtering schemes, as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific data, then decode using noisy channel model reranking. Our system improves on our previous system{'}s performance by 4.5 BLEU points and achieves the best case-sensitive BLEU score for the translation direction English→Russian.",
}
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%0 Conference Proceedings
%T Facebook FAIR’s WMT19 News Translation Task Submission
%A Ng, Nathan
%A Yee, Kyra
%A Baevski, Alexei
%A Ott, Myle
%A Auli, Michael
%A Edunov, Sergey
%S Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F ng-etal-2019-facebook
%X This paper describes Facebook FAIR’s submission to the WMT19 shared news translation task. We participate in four language directions, English \textless-\textgreater German and English \textless-\textgreater Russian in both directions. Following our submission from last year, our baseline systems are large BPE-based transformer models trained with the FAIRSEQ sequence modeling toolkit. This year we experiment with different bitext data filtering schemes, as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific data, then decode using noisy channel model reranking. Our system improves on our previous system’s performance by 4.5 BLEU points and achieves the best case-sensitive BLEU score for the translation direction English→Russian.
%R 10.18653/v1/W19-5333
%U https://aclanthology.org/W19-5333
%U https://doi.org/10.18653/v1/W19-5333
%P 314-319
Markdown (Informal)
[Facebook FAIR’s WMT19 News Translation Task Submission](https://aclanthology.org/W19-5333) (Ng et al., WMT 2019)
ACL
- Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, and Sergey Edunov. 2019. Facebook FAIR’s WMT19 News Translation Task Submission. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 314–319, Florence, Italy. Association for Computational Linguistics.