Improved Data Augmentation for Translation Suggestion
Hongxiao Zhang, Siyu Lai, Songming Zhang, Hui Huang, Yufeng Chen, Jinan Xu, Jian Liu
Abstract
Translation suggestion (TS) models are used to automatically provide alternative suggestions for incorrect spans in sentences generated by machine translation. This paper introduces the system used in our submission to the WMT’22 Translation Suggestion shared task. Our system is based on the ensemble of different translation architectures, including Transformer, SA-Transformer, and DynamicConv. We use three strategies to construct synthetic data from parallel corpora to compensate for the lack of supervised data. In addition, we introduce a multi-phase pre-training strategy, adding an additional pre-training phase with in-domain data. We rank second and third on the English-German and English-Chinese bidirectional tasks, respectively.- Anthology ID:
- 2022.wmt-1.125
- 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:
- 1211–1216
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.125
- DOI:
- Bibkey:
- Cite (ACL):
- Hongxiao Zhang, Siyu Lai, Songming Zhang, Hui Huang, Yufeng Chen, Jinan Xu, and Jian Liu. 2022. Improved Data Augmentation for Translation Suggestion. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1211–1216, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Improved Data Augmentation for Translation Suggestion (Zhang et al., WMT 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.wmt-1.125.pdf
Export citation
@inproceedings{zhang-etal-2022-improved,
title = "Improved Data Augmentation for Translation Suggestion",
author = "Zhang, Hongxiao and
Lai, Siyu and
Zhang, Songming and
Huang, Hui and
Chen, Yufeng and
Xu, Jinan and
Liu, Jian",
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-1.125",
pages = "1211--1216",
abstract = "Translation suggestion (TS) models are used to automatically provide alternative suggestions for incorrect spans in sentences generated by machine translation. This paper introduces the system used in our submission to the WMT{'}22 Translation Suggestion shared task. Our system is based on the ensemble of different translation architectures, including Transformer, SA-Transformer, and DynamicConv. We use three strategies to construct synthetic data from parallel corpora to compensate for the lack of supervised data. In addition, we introduce a multi-phase pre-training strategy, adding an additional pre-training phase with in-domain data. We rank second and third on the English-German and English-Chinese bidirectional tasks, respectively.",
}
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%0 Conference Proceedings %T Improved Data Augmentation for Translation Suggestion %A Zhang, Hongxiao %A Lai, Siyu %A Zhang, Songming %A Huang, Hui %A Chen, Yufeng %A Xu, Jinan %A Liu, Jian %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 zhang-etal-2022-improved %X Translation suggestion (TS) models are used to automatically provide alternative suggestions for incorrect spans in sentences generated by machine translation. This paper introduces the system used in our submission to the WMT’22 Translation Suggestion shared task. Our system is based on the ensemble of different translation architectures, including Transformer, SA-Transformer, and DynamicConv. We use three strategies to construct synthetic data from parallel corpora to compensate for the lack of supervised data. In addition, we introduce a multi-phase pre-training strategy, adding an additional pre-training phase with in-domain data. We rank second and third on the English-German and English-Chinese bidirectional tasks, respectively. %U https://aclanthology.org/2022.wmt-1.125 %P 1211-1216
Markdown (Informal)
[Improved Data Augmentation for Translation Suggestion](https://aclanthology.org/2022.wmt-1.125) (Zhang et al., WMT 2022)
- Improved Data Augmentation for Translation Suggestion (Zhang et al., WMT 2022)
ACL
- Hongxiao Zhang, Siyu Lai, Songming Zhang, Hui Huang, Yufeng Chen, Jinan Xu, and Jian Liu. 2022. Improved Data Augmentation for Translation Suggestion. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1211–1216, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.