Facebook AI’s WMT20 News Translation Task Submission
Peng-Jen Chen, Ann Lee, Changhan Wang, Naman Goyal, Angela Fan, Mary Williamson, Jiatao Gu
Correct Metadata for
Abstract
This paper describes Facebook AI’s submission to WMT20 shared news translation task. We focus on the low resource setting and participate in two language pairs, Tamil <-> English and Inuktitut <-> English, where there are limited out-of-domain bitext and monolingual data. We approach the low resource problem using two main strategies, leveraging all available data and adapting the system to the target news domain. We explore techniques that leverage bitext and monolingual data from all languages, such as self-supervised model pretraining, multilingual models, data augmentation, and reranking. To better adapt the translation system to the test domain, we explore dataset tagging and fine-tuning on in-domain data. We observe that different techniques provide varied improvements based on the available data of the language pair. Based on the finding, we integrate these techniques into one training pipeline. For En->Ta, we explore an unconstrained setup with additional Tamil bitext and monolingual data and show that further improvement can be obtained. On the test set, our best submitted systems achieve 21.5 and 13.7 BLEU for Ta->En and En->Ta respectively, and 27.9 and 13.0 for Iu->En and En->Iu respectively.- Anthology ID:
- 2020.wmt-1.8
- Volume:
- Proceedings of the Fifth Conference on Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Yvette Graham, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 113–125
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.8/
- DOI:
- Bibkey:
- Cite (ACL):
- Peng-Jen Chen, Ann Lee, Changhan Wang, Naman Goyal, Angela Fan, Mary Williamson, and Jiatao Gu. 2020. Facebook AI’s WMT20 News Translation Task Submission. In Proceedings of the Fifth Conference on Machine Translation, pages 113–125, Online. Association for Computational Linguistics.
- Cite (Informal):
- Facebook AI’s WMT20 News Translation Task Submission (Chen et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.8.pdf
- Video:
- https://slideslive.com/38939624
Export citation
@inproceedings{chen-etal-2020-facebook,
title = "{F}acebook {AI}`s {WMT}20 News Translation Task Submission",
author = "Chen, Peng-Jen and
Lee, Ann and
Wang, Changhan and
Goyal, Naman and
Fan, Angela and
Williamson, Mary and
Gu, Jiatao",
editor = {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
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.8/",
pages = "113--125",
abstract = "This paper describes Facebook AI`s submission to WMT20 shared news translation task. We focus on the low resource setting and participate in two language pairs, Tamil {\ensuremath{<}}-{\ensuremath{>}} English and Inuktitut {\ensuremath{<}}-{\ensuremath{>}} English, where there are limited out-of-domain bitext and monolingual data. We approach the low resource problem using two main strategies, leveraging all available data and adapting the system to the target news domain. We explore techniques that leverage bitext and monolingual data from all languages, such as self-supervised model pretraining, multilingual models, data augmentation, and reranking. To better adapt the translation system to the test domain, we explore dataset tagging and fine-tuning on in-domain data. We observe that different techniques provide varied improvements based on the available data of the language pair. Based on the finding, we integrate these techniques into one training pipeline. For En-{\ensuremath{>}}Ta, we explore an unconstrained setup with additional Tamil bitext and monolingual data and show that further improvement can be obtained. On the test set, our best submitted systems achieve 21.5 and 13.7 BLEU for Ta-{\ensuremath{>}}En and En-{\ensuremath{>}}Ta respectively, and 27.9 and 13.0 for Iu-{\ensuremath{>}}En and En-{\ensuremath{>}}Iu respectively."
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<abstract>This paper describes Facebook AI‘s submission to WMT20 shared news translation task. We focus on the low resource setting and participate in two language pairs, Tamil \ensuremath<-\ensuremath> English and Inuktitut \ensuremath<-\ensuremath> English, where there are limited out-of-domain bitext and monolingual data. We approach the low resource problem using two main strategies, leveraging all available data and adapting the system to the target news domain. We explore techniques that leverage bitext and monolingual data from all languages, such as self-supervised model pretraining, multilingual models, data augmentation, and reranking. To better adapt the translation system to the test domain, we explore dataset tagging and fine-tuning on in-domain data. We observe that different techniques provide varied improvements based on the available data of the language pair. Based on the finding, we integrate these techniques into one training pipeline. For En-\ensuremath>Ta, we explore an unconstrained setup with additional Tamil bitext and monolingual data and show that further improvement can be obtained. On the test set, our best submitted systems achieve 21.5 and 13.7 BLEU for Ta-\ensuremath>En and En-\ensuremath>Ta respectively, and 27.9 and 13.0 for Iu-\ensuremath>En and En-\ensuremath>Iu respectively.</abstract>
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%0 Conference Proceedings %T Facebook AI‘s WMT20 News Translation Task Submission %A Chen, Peng-Jen %A Lee, Ann %A Wang, Changhan %A Goyal, Naman %A Fan, Angela %A Williamson, Mary %A Gu, Jiatao %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 Graham, Yvette %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %S Proceedings of the Fifth Conference on Machine Translation %D 2020 %8 November %I Association for Computational Linguistics %C Online %F chen-etal-2020-facebook %X This paper describes Facebook AI‘s submission to WMT20 shared news translation task. We focus on the low resource setting and participate in two language pairs, Tamil \ensuremath<-\ensuremath> English and Inuktitut \ensuremath<-\ensuremath> English, where there are limited out-of-domain bitext and monolingual data. We approach the low resource problem using two main strategies, leveraging all available data and adapting the system to the target news domain. We explore techniques that leverage bitext and monolingual data from all languages, such as self-supervised model pretraining, multilingual models, data augmentation, and reranking. To better adapt the translation system to the test domain, we explore dataset tagging and fine-tuning on in-domain data. We observe that different techniques provide varied improvements based on the available data of the language pair. Based on the finding, we integrate these techniques into one training pipeline. For En-\ensuremath>Ta, we explore an unconstrained setup with additional Tamil bitext and monolingual data and show that further improvement can be obtained. On the test set, our best submitted systems achieve 21.5 and 13.7 BLEU for Ta-\ensuremath>En and En-\ensuremath>Ta respectively, and 27.9 and 13.0 for Iu-\ensuremath>En and En-\ensuremath>Iu respectively. %U https://aclanthology.org/2020.wmt-1.8/ %P 113-125
Markdown (Informal)
[Facebook AI’s WMT20 News Translation Task Submission](https://aclanthology.org/2020.wmt-1.8/) (Chen et al., WMT 2020)
- Facebook AI’s WMT20 News Translation Task Submission (Chen et al., WMT 2020)
ACL
- Peng-Jen Chen, Ann Lee, Changhan Wang, Naman Goyal, Angela Fan, Mary Williamson, and Jiatao Gu. 2020. Facebook AI’s WMT20 News Translation Task Submission. In Proceedings of the Fifth Conference on Machine Translation, pages 113–125, Online. Association for Computational Linguistics.