The SPECTRANS System Description for the WMT22 Biomedical Task
Nicolas Ballier, Jean-baptiste Yunès, Guillaume Wisniewski, Lichao Zhu, Maria Zimina
Correct Metadata for
Abstract
This paper describes the SPECTRANS submission for the WMT 2022 biomedical shared task. We present the results of our experiments using the training corpora and the JoeyNMT (Kreutzer et al., 2019) and SYSTRAN Pure Neural Server/ Advanced Model Studio toolkits for the language directions English to French and French to English. We compare the pre- dictions of the different toolkits. We also use JoeyNMT to fine-tune the model with a selection of texts from WMT, Khresmoi and UFAL data sets. We report our results and assess the respective merits of the different translated texts.- Anthology ID:
- 2022.wmt-1.82
- 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:
- 895–900
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.82/
- DOI:
- Bibkey:
- Cite (ACL):
- Nicolas Ballier, Jean-baptiste Yunès, Guillaume Wisniewski, Lichao Zhu, and Maria Zimina. 2022. The SPECTRANS System Description for the WMT22 Biomedical Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 895–900, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- The SPECTRANS System Description for the WMT22 Biomedical Task (Ballier et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.82.pdf
Export citation
@inproceedings{ballier-etal-2022-spectrans,
title = "The {SPECTRANS} System Description for the {WMT}22 Biomedical Task",
author = "Ballier, Nicolas and
Yun{\`e}s, Jean-baptiste and
Wisniewski, Guillaume and
Zhu, Lichao and
Zimina, Maria",
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-1.82/",
pages = "895--900",
abstract = "This paper describes the SPECTRANS submission for the WMT 2022 biomedical shared task. We present the results of our experiments using the training corpora and the JoeyNMT (Kreutzer et al., 2019) and SYSTRAN Pure Neural Server/ Advanced Model Studio toolkits for the language directions English to French and French to English. We compare the pre- dictions of the different toolkits. We also use JoeyNMT to fine-tune the model with a selection of texts from WMT, Khresmoi and UFAL data sets. We report our results and assess the respective merits of the different translated texts."
}
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%0 Conference Proceedings %T The SPECTRANS System Description for the WMT22 Biomedical Task %A Ballier, Nicolas %A Yunès, Jean-baptiste %A Wisniewski, Guillaume %A Zhu, Lichao %A Zimina, Maria %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 ballier-etal-2022-spectrans %X This paper describes the SPECTRANS submission for the WMT 2022 biomedical shared task. We present the results of our experiments using the training corpora and the JoeyNMT (Kreutzer et al., 2019) and SYSTRAN Pure Neural Server/ Advanced Model Studio toolkits for the language directions English to French and French to English. We compare the pre- dictions of the different toolkits. We also use JoeyNMT to fine-tune the model with a selection of texts from WMT, Khresmoi and UFAL data sets. We report our results and assess the respective merits of the different translated texts. %U https://aclanthology.org/2022.wmt-1.82/ %P 895-900
Markdown (Informal)
[The SPECTRANS System Description for the WMT22 Biomedical Task](https://aclanthology.org/2022.wmt-1.82/) (Ballier et al., WMT 2022)
- The SPECTRANS System Description for the WMT22 Biomedical Task (Ballier et al., WMT 2022)
ACL
- Nicolas Ballier, Jean-baptiste Yunès, Guillaume Wisniewski, Lichao Zhu, and Maria Zimina. 2022. The SPECTRANS System Description for the WMT22 Biomedical Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 895–900, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.