Ensembling of Distilled Models from Multi-task Teachers for Constrained Resource Language Pairs
Amr Hendy, Esraa A. Gad, Mohamed Abdelghaffar, Jailan S. ElMosalami, Mohamed Afify, Ahmed Y. Tawfik, Hany Hassan Awadalla
Correct Metadata for
Abstract
This paper describes the Microsoft Egypt Development Center (EgDC) submission to the constrained track of WMT21 shared news translation task. We focus on the three relatively low resource language pairs Bengali ↔ Hindi, English ↔ Hausa and Xhosa ↔ Zulu. To overcome the limitation of relatively low parallel data we train a multilingual model using a multitask objective employing both parallel and monolingual data. In addition, we augment the data using back translation. We also train a bilingual model incorporating back translation and knowledge distillation then combine the two models using sequence-to-sequence mapping. We see around 70% relative gain in BLEU point for En ↔ Ha and around 25% relative improvements for Bn ↔ Hi and Xh ↔ Zu compared to bilingual baselines.- Anthology ID:
- 2021.wmt-1.8
- 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:
- 130–135
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.8/
- DOI:
- Bibkey:
- Cite (ACL):
- Amr Hendy, Esraa A. Gad, Mohamed Abdelghaffar, Jailan S. ElMosalami, Mohamed Afify, Ahmed Y. Tawfik, and Hany Hassan Awadalla. 2021. Ensembling of Distilled Models from Multi-task Teachers for Constrained Resource Language Pairs. In Proceedings of the Sixth Conference on Machine Translation, pages 130–135, Online. Association for Computational Linguistics.
- Cite (Informal):
- Ensembling of Distilled Models from Multi-task Teachers for Constrained Resource Language Pairs (Hendy et al., WMT 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.wmt-1.8.pdf
Export citation
@inproceedings{hendy-etal-2021-ensembling,
title = "Ensembling of Distilled Models from Multi-task Teachers for Constrained Resource Language Pairs",
author = "Hendy, Amr and
Gad, Esraa A. and
Abdelghaffar, Mohamed and
ElMosalami, Jailan S. and
Afify, Mohamed and
Tawfik, Ahmed Y. and
Hassan Awadalla, Hany",
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-1.8/",
pages = "130--135",
abstract = "This paper describes the Microsoft Egypt Development Center (EgDC) submission to the constrained track of WMT21 shared news translation task. We focus on the three relatively low resource language pairs Bengali {\ensuremath{\leftrightarrow}} Hindi, English {\ensuremath{\leftrightarrow}} Hausa and Xhosa {\ensuremath{\leftrightarrow}} Zulu. To overcome the limitation of relatively low parallel data we train a multilingual model using a multitask objective employing both parallel and monolingual data. In addition, we augment the data using back translation. We also train a bilingual model incorporating back translation and knowledge distillation then combine the two models using sequence-to-sequence mapping. We see around 70{\%} relative gain in BLEU point for En {\ensuremath{\leftrightarrow}} Ha and around 25{\%} relative improvements for Bn {\ensuremath{\leftrightarrow}} Hi and Xh {\ensuremath{\leftrightarrow}} Zu compared to bilingual baselines."
}
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<abstract>This paper describes the Microsoft Egypt Development Center (EgDC) submission to the constrained track of WMT21 shared news translation task. We focus on the three relatively low resource language pairs Bengali \ensuremathłeftrightarrow Hindi, English \ensuremathłeftrightarrow Hausa and Xhosa \ensuremathłeftrightarrow Zulu. To overcome the limitation of relatively low parallel data we train a multilingual model using a multitask objective employing both parallel and monolingual data. In addition, we augment the data using back translation. We also train a bilingual model incorporating back translation and knowledge distillation then combine the two models using sequence-to-sequence mapping. We see around 70% relative gain in BLEU point for En \ensuremathłeftrightarrow Ha and around 25% relative improvements for Bn \ensuremathłeftrightarrow Hi and Xh \ensuremathłeftrightarrow Zu compared to bilingual baselines.</abstract>
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%0 Conference Proceedings %T Ensembling of Distilled Models from Multi-task Teachers for Constrained Resource Language Pairs %A Hendy, Amr %A Gad, Esraa A. %A Abdelghaffar, Mohamed %A ElMosalami, Jailan S. %A Afify, Mohamed %A Tawfik, Ahmed Y. %A Hassan Awadalla, Hany %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 hendy-etal-2021-ensembling %X This paper describes the Microsoft Egypt Development Center (EgDC) submission to the constrained track of WMT21 shared news translation task. We focus on the three relatively low resource language pairs Bengali \ensuremathłeftrightarrow Hindi, English \ensuremathłeftrightarrow Hausa and Xhosa \ensuremathłeftrightarrow Zulu. To overcome the limitation of relatively low parallel data we train a multilingual model using a multitask objective employing both parallel and monolingual data. In addition, we augment the data using back translation. We also train a bilingual model incorporating back translation and knowledge distillation then combine the two models using sequence-to-sequence mapping. We see around 70% relative gain in BLEU point for En \ensuremathłeftrightarrow Ha and around 25% relative improvements for Bn \ensuremathłeftrightarrow Hi and Xh \ensuremathłeftrightarrow Zu compared to bilingual baselines. %U https://aclanthology.org/2021.wmt-1.8/ %P 130-135
Markdown (Informal)
[Ensembling of Distilled Models from Multi-task Teachers for Constrained Resource Language Pairs](https://aclanthology.org/2021.wmt-1.8/) (Hendy et al., WMT 2021)
- Ensembling of Distilled Models from Multi-task Teachers for Constrained Resource Language Pairs (Hendy et al., WMT 2021)
ACL
- Amr Hendy, Esraa A. Gad, Mohamed Abdelghaffar, Jailan S. ElMosalami, Mohamed Afify, Ahmed Y. Tawfik, and Hany Hassan Awadalla. 2021. Ensembling of Distilled Models from Multi-task Teachers for Constrained Resource Language Pairs. In Proceedings of the Sixth Conference on Machine Translation, pages 130–135, Online. Association for Computational Linguistics.