Abstract
We describe our NMT system submitted to the WMT2018 shared task in news translation. Our system is based on the Transformer model (Vaswani et al., 2017). We use an improved technique of backtranslation, where we iterate the process of translating monolingual data in one direction and training an NMT model for the opposite direction using synthetic parallel data. We apply a simple but effective filtering of the synthetic data. We pre-process the input sentences using coreference resolution in order to disambiguate the gender of pro-dropped personal pronouns. Finally, we apply two simple post-processing substitutions on the translated output. Our system is significantly (p < 0.05) better than all other English-Czech and Czech-English systems in WMT2018.- Anthology ID:
- W18-6424
- Volume:
- Proceedings of the Third Conference on Machine Translation: Shared Task Papers
- Month:
- October
- Year:
- 2018
- Address:
- Belgium, Brussels
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 482–487
- Language:
- URL:
- https://aclanthology.org/W18-6424
- DOI:
- 10.18653/v1/W18-6424
- Bibkey:
- Cite (ACL):
- Martin Popel. 2018. CUNI Transformer Neural MT System for WMT18. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 482–487, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- CUNI Transformer Neural MT System for WMT18 (Popel, WMT 2018)
- Copy Citation:
- PDF:
- https://aclanthology.org/W18-6424.pdf
Export citation
@inproceedings{popel-2018-cuni,
title = "{CUNI} Transformer Neural {MT} System for {WMT}18",
author = "Popel, Martin",
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
Monz, Christof and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Post, Matt and
Specia, Lucia and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6424",
doi = "10.18653/v1/W18-6424",
pages = "482--487",
abstract = "We describe our NMT system submitted to the WMT2018 shared task in news translation. Our system is based on the Transformer model (Vaswani et al., 2017). We use an improved technique of backtranslation, where we iterate the process of translating monolingual data in one direction and training an NMT model for the opposite direction using synthetic parallel data. We apply a simple but effective filtering of the synthetic data. We pre-process the input sentences using coreference resolution in order to disambiguate the gender of pro-dropped personal pronouns. Finally, we apply two simple post-processing substitutions on the translated output. Our system is significantly (p {\textless} 0.05) better than all other English-Czech and Czech-English systems in WMT2018.",
}
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%0 Conference Proceedings %T CUNI Transformer Neural MT System for WMT18 %A Popel, Martin %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 Monz, Christof %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Post, Matt %Y Specia, Lucia %Y Turchi, Marco %Y Verspoor, Karin %S Proceedings of the Third Conference on Machine Translation: Shared Task Papers %D 2018 %8 October %I Association for Computational Linguistics %C Belgium, Brussels %F popel-2018-cuni %X We describe our NMT system submitted to the WMT2018 shared task in news translation. Our system is based on the Transformer model (Vaswani et al., 2017). We use an improved technique of backtranslation, where we iterate the process of translating monolingual data in one direction and training an NMT model for the opposite direction using synthetic parallel data. We apply a simple but effective filtering of the synthetic data. We pre-process the input sentences using coreference resolution in order to disambiguate the gender of pro-dropped personal pronouns. Finally, we apply two simple post-processing substitutions on the translated output. Our system is significantly (p \textless 0.05) better than all other English-Czech and Czech-English systems in WMT2018. %R 10.18653/v1/W18-6424 %U https://aclanthology.org/W18-6424 %U https://doi.org/10.18653/v1/W18-6424 %P 482-487
Markdown (Informal)
[CUNI Transformer Neural MT System for WMT18](https://aclanthology.org/W18-6424) (Popel, WMT 2018)
- CUNI Transformer Neural MT System for WMT18 (Popel, WMT 2018)
ACL
- Martin Popel. 2018. CUNI Transformer Neural MT System for WMT18. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 482–487, Belgium, Brussels. Association for Computational Linguistics.