Huawei BabelTar NMT at WMT22 Biomedical Translation Task: How We Further Improve Domain-specific NMT
Abstract
This paper describes Huawei Artificial Intelligence Application Research Center’s neural machine translation system (“BabelTar”). Our submission to the WMT22 biomedical translation shared task covers language directions between English and the other seven languages (French, German, Italian, Spanish, Portuguese, Russian, and Chinese). During the past four years, our participation in this domain-specific track has witnessed a paradigm shift of methodology from a purely data-driven focus to embracing diversified techniques, including pre-trained multilingual NMT models, homograph disambiguation, ensemble learning, and preprocessing methods. We illustrate practical insights and measured performance improvements relating to how we further improve our domain-specific NMT system.- Anthology ID:
- 2022.wmt-1.87
- Volume:
- Proceedings of the Seventh Conference on Machine Translation (WMT)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Marco Turchi, Marcos Zampieri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 930–935
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.87
- DOI:
- Bibkey:
- Cite (ACL):
- Weixuan Wang, Xupeng Meng, Suqing Yan, Ye Tian, and Wei Peng. 2022. Huawei BabelTar NMT at WMT22 Biomedical Translation Task: How We Further Improve Domain-specific NMT. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 930–935, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Huawei BabelTar NMT at WMT22 Biomedical Translation Task: How We Further Improve Domain-specific NMT (Wang et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.87.pdf
Export citation
@inproceedings{wang-etal-2022-huawei,
title = "Huawei {B}abel{T}ar {NMT} at {WMT}22 Biomedical Translation Task: How We Further Improve Domain-specific {NMT}",
author = "Wang, Weixuan and
Meng, Xupeng and
Yan, Suqing and
Tian, Ye and
Peng, Wei",
editor = {Koehn, Philipp and
Barrault, Lo{\"\i}c and
Bojar, Ond{\v{r}}ej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss{\`a}, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Freitag, Markus and
Graham, Yvette and
Grundkiewicz, Roman and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Kocmi, Tom and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Popel, Martin and
Turchi, Marco and
Zampieri, Marcos},
booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wmt-1.87",
pages = "930--935",
abstract = "This paper describes Huawei Artificial Intelligence Application Research Center{'}s neural machine translation system ({``}BabelTar{''}). Our submission to the WMT22 biomedical translation shared task covers language directions between English and the other seven languages (French, German, Italian, Spanish, Portuguese, Russian, and Chinese). During the past four years, our participation in this domain-specific track has witnessed a paradigm shift of methodology from a purely data-driven focus to embracing diversified techniques, including pre-trained multilingual NMT models, homograph disambiguation, ensemble learning, and preprocessing methods. We illustrate practical insights and measured performance improvements relating to how we further improve our domain-specific NMT system.",
}
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%0 Conference Proceedings %T Huawei BabelTar NMT at WMT22 Biomedical Translation Task: How We Further Improve Domain-specific NMT %A Wang, Weixuan %A Meng, Xupeng %A Yan, Suqing %A Tian, Ye %A Peng, Wei %Y Koehn, Philipp %Y Barrault, Loïc %Y Bojar, Ondřej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussà, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Freitag, Markus %Y Graham, Yvette %Y Grundkiewicz, Roman %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Jimeno Yepes, Antonio %Y Kocmi, Tom %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Popel, Martin %Y Turchi, Marco %Y Zampieri, Marcos %S Proceedings of the Seventh Conference on Machine Translation (WMT) %D 2022 %8 December %I Association for Computational Linguistics %C Abu Dhabi, United Arab Emirates (Hybrid) %F wang-etal-2022-huawei %X This paper describes Huawei Artificial Intelligence Application Research Center’s neural machine translation system (“BabelTar”). Our submission to the WMT22 biomedical translation shared task covers language directions between English and the other seven languages (French, German, Italian, Spanish, Portuguese, Russian, and Chinese). During the past four years, our participation in this domain-specific track has witnessed a paradigm shift of methodology from a purely data-driven focus to embracing diversified techniques, including pre-trained multilingual NMT models, homograph disambiguation, ensemble learning, and preprocessing methods. We illustrate practical insights and measured performance improvements relating to how we further improve our domain-specific NMT system. %U https://aclanthology.org/2022.wmt-1.87 %P 930-935
Markdown (Informal)
[Huawei BabelTar NMT at WMT22 Biomedical Translation Task: How We Further Improve Domain-specific NMT](https://aclanthology.org/2022.wmt-1.87) (Wang et al., WMT 2022)
- Huawei BabelTar NMT at WMT22 Biomedical Translation Task: How We Further Improve Domain-specific NMT (Wang et al., WMT 2022)
ACL
- Weixuan Wang, Xupeng Meng, Suqing Yan, Ye Tian, and Wei Peng. 2022. Huawei BabelTar NMT at WMT22 Biomedical Translation Task: How We Further Improve Domain-specific NMT. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 930–935, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.