TenTrans Multilingual Low-Resource Translation System for WMT21 Indo-European Languages Task
Han Yang, Bojie Hu, Wanying Xie, Ambyera Han, Pan Liu, Jinan Xu, Qi Ju
Abstract
This paper describes TenTrans’ submission to WMT21 Multilingual Low-Resource Translation shared task for the Romance language pairs. This task focuses on improving translation quality from Catalan to Occitan, Romanian and Italian, with the assistance of related high-resource languages. We mainly utilize back-translation, pivot-based methods, multilingual models, pre-trained model fine-tuning, and in-domain knowledge transfer to improve the translation quality. On the test set, our best-submitted system achieves an average of 43.45 case-sensitive BLEU scores across all low-resource pairs. Our data, code, and pre-trained models used in this work are available in TenTrans evaluation examples.- Anthology ID:
- 2021.wmt-1.45
- Volume:
- Proceedings of the Sixth Conference on Machine Translation
- Month:
- November
- Year:
- 2021
- Address:
- Online
- Editors:
- Loic Barrault, Ondrej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussa, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Tom Kocmi, Andre Martins, Makoto Morishita, Christof Monz
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 376–382
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.45
- DOI:
- Bibkey:
- Cite (ACL):
- Han Yang, Bojie Hu, Wanying Xie, Ambyera Han, Pan Liu, Jinan Xu, and Qi Ju. 2021. TenTrans Multilingual Low-Resource Translation System for WMT21 Indo-European Languages Task. In Proceedings of the Sixth Conference on Machine Translation, pages 376–382, Online. Association for Computational Linguistics.
- Cite (Informal):
- TenTrans Multilingual Low-Resource Translation System for WMT21 Indo-European Languages Task (Yang et al., WMT 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.wmt-1.45.pdf
Export citation
@inproceedings{yang-etal-2021-tentrans,
title = "{T}en{T}rans Multilingual Low-Resource Translation System for {WMT}21 {I}ndo-{E}uropean Languages Task",
author = "Yang, Han and
Hu, Bojie and
Xie, Wanying and
Han, Ambyera and
Liu, Pan and
Xu, Jinan and
Ju, Qi",
editor = "Barrault, Loic and
Bojar, Ondrej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-jussa, 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
Yepes, Antonio Jimeno and
Koehn, Philipp and
Kocmi, Tom and
Martins, Andre and
Morishita, Makoto and
Monz, Christof",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wmt-1.45",
pages = "376--382",
abstract = "This paper describes TenTrans{'} submission to WMT21 Multilingual Low-Resource Translation shared task for the Romance language pairs. This task focuses on improving translation quality from Catalan to Occitan, Romanian and Italian, with the assistance of related high-resource languages. We mainly utilize back-translation, pivot-based methods, multilingual models, pre-trained model fine-tuning, and in-domain knowledge transfer to improve the translation quality. On the test set, our best-submitted system achieves an average of 43.45 case-sensitive BLEU scores across all low-resource pairs. Our data, code, and pre-trained models used in this work are available in TenTrans evaluation examples.",
}
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%0 Conference Proceedings %T TenTrans Multilingual Low-Resource Translation System for WMT21 Indo-European Languages Task %A Yang, Han %A Hu, Bojie %A Xie, Wanying %A Han, Ambyera %A Liu, Pan %A Xu, Jinan %A Ju, Qi %Y Barrault, Loic %Y Bojar, Ondrej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussa, 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 Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Kocmi, Tom %Y Martins, Andre %Y Morishita, Makoto %Y Monz, Christof %S Proceedings of the Sixth Conference on Machine Translation %D 2021 %8 November %I Association for Computational Linguistics %C Online %F yang-etal-2021-tentrans %X This paper describes TenTrans’ submission to WMT21 Multilingual Low-Resource Translation shared task for the Romance language pairs. This task focuses on improving translation quality from Catalan to Occitan, Romanian and Italian, with the assistance of related high-resource languages. We mainly utilize back-translation, pivot-based methods, multilingual models, pre-trained model fine-tuning, and in-domain knowledge transfer to improve the translation quality. On the test set, our best-submitted system achieves an average of 43.45 case-sensitive BLEU scores across all low-resource pairs. Our data, code, and pre-trained models used in this work are available in TenTrans evaluation examples. %U https://aclanthology.org/2021.wmt-1.45 %P 376-382
Markdown (Informal)
[TenTrans Multilingual Low-Resource Translation System for WMT21 Indo-European Languages Task](https://aclanthology.org/2021.wmt-1.45) (Yang et al., WMT 2021)
- TenTrans Multilingual Low-Resource Translation System for WMT21 Indo-European Languages Task (Yang et al., WMT 2021)
ACL
- Han Yang, Bojie Hu, Wanying Xie, Ambyera Han, Pan Liu, Jinan Xu, and Qi Ju. 2021. TenTrans Multilingual Low-Resource Translation System for WMT21 Indo-European Languages Task. In Proceedings of the Sixth Conference on Machine Translation, pages 376–382, Online. Association for Computational Linguistics.