Facebook FAIR’s WMT19 News Translation Task Submission
Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov
Correct Metadata for
Abstract
This paper describes Facebook FAIR’s submission to the WMT19 shared news translation task. We participate in four language directions, English <-> German and English <-> 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.- Anthology ID:
- W19-5333
- Volume:
- Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Marco Turchi, Karin Verspoor
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 314–319
- Language:
- URL:
- https://aclanthology.org/W19-5333/
- DOI:
- 10.18653/v1/W19-5333
- Bibkey:
- Cite (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.
- Cite (Informal):
- Facebook FAIR’s WMT19 News Translation Task Submission (Ng et al., WMT 2019)
- Copy Citation:
- PDF:
- https://aclanthology.org/W19-5333.pdf
- Code
- additional community code
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@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",
editor = "Bojar, Ond{\v{r}}ej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Monz, Christof and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Post, Matt and
Turchi, Marco and
Verspoor, Karin",
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 {\ensuremath{<}}-{\ensuremath{>}} German and English {\ensuremath{<}}-{\ensuremath{>}} 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{\textrightarrow}Russian."
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<abstract>This paper describes Facebook FAIR‘s submission to the WMT19 shared news translation task. We participate in four language directions, English \ensuremath<-\ensuremath> German and English \ensuremath<-\ensuremath> 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.</abstract>
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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 %Y Bojar, Ondřej %Y Chatterjee, Rajen %Y Federmann, Christian %Y Fishel, Mark %Y Graham, Yvette %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Martins, André %Y Monz, Christof %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Post, Matt %Y Turchi, Marco %Y Verspoor, Karin %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 \ensuremath<-\ensuremath> German and English \ensuremath<-\ensuremath> 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)
- Facebook FAIR’s WMT19 News Translation Task Submission (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.