@inproceedings{chen-etal-2019-facebook,
title = "{F}acebook {AI}{'}s {WAT}19 {M}yanmar-{E}nglish Translation Task Submission",
author = "Chen, Peng-Jen and
Shen, Jiajun and
Le, Matthew and
Chaudhary, Vishrav and
El-Kishky, Ahmed and
Wenzek, Guillaume and
Ott, Myle and
Ranzato, Marc{'}Aurelio",
editor = "Nakazawa, Toshiaki and
Ding, Chenchen and
Dabre, Raj and
Kunchukuttan, Anoop and
Doi, Nobushige and
Oda, Yusuke and
Bojar, Ond{\v{r}}ej and
Parida, Shantipriya and
Goto, Isao and
Mino, Hidaya",
booktitle = "Proceedings of the 6th Workshop on Asian Translation",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5213/",
doi = "10.18653/v1/D19-5213",
pages = "112--122",
abstract = "This paper describes Facebook AI{'}s submission to the WAT 2019 Myanmar-English translation task. Our baseline systems are BPE-based transformer models. We explore methods to leverage monolingual data to improve generalization, including self-training, back-translation and their combination. We further improve results by using noisy channel re-ranking and ensembling. We demonstrate that these techniques can significantly improve not only a system trained with additional monolingual data, but even the baseline system trained exclusively on the provided small parallel dataset. Our system ranks first in both directions according to human evaluation and BLEU, with a gain of over 8 BLEU points above the second best system."
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<abstract>This paper describes Facebook AI’s submission to the WAT 2019 Myanmar-English translation task. Our baseline systems are BPE-based transformer models. We explore methods to leverage monolingual data to improve generalization, including self-training, back-translation and their combination. We further improve results by using noisy channel re-ranking and ensembling. We demonstrate that these techniques can significantly improve not only a system trained with additional monolingual data, but even the baseline system trained exclusively on the provided small parallel dataset. Our system ranks first in both directions according to human evaluation and BLEU, with a gain of over 8 BLEU points above the second best system.</abstract>
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%0 Conference Proceedings
%T Facebook AI’s WAT19 Myanmar-English Translation Task Submission
%A Chen, Peng-Jen
%A Shen, Jiajun
%A Le, Matthew
%A Chaudhary, Vishrav
%A El-Kishky, Ahmed
%A Wenzek, Guillaume
%A Ott, Myle
%A Ranzato, Marc’Aurelio
%Y Nakazawa, Toshiaki
%Y Ding, Chenchen
%Y Dabre, Raj
%Y Kunchukuttan, Anoop
%Y Doi, Nobushige
%Y Oda, Yusuke
%Y Bojar, Ondřej
%Y Parida, Shantipriya
%Y Goto, Isao
%Y Mino, Hidaya
%S Proceedings of the 6th Workshop on Asian Translation
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F chen-etal-2019-facebook
%X This paper describes Facebook AI’s submission to the WAT 2019 Myanmar-English translation task. Our baseline systems are BPE-based transformer models. We explore methods to leverage monolingual data to improve generalization, including self-training, back-translation and their combination. We further improve results by using noisy channel re-ranking and ensembling. We demonstrate that these techniques can significantly improve not only a system trained with additional monolingual data, but even the baseline system trained exclusively on the provided small parallel dataset. Our system ranks first in both directions according to human evaluation and BLEU, with a gain of over 8 BLEU points above the second best system.
%R 10.18653/v1/D19-5213
%U https://aclanthology.org/D19-5213/
%U https://doi.org/10.18653/v1/D19-5213
%P 112-122
Markdown (Informal)
[Facebook AI’s WAT19 Myanmar-English Translation Task Submission](https://aclanthology.org/D19-5213/) (Chen et al., WAT 2019)
ACL
- Peng-Jen Chen, Jiajun Shen, Matthew Le, Vishrav Chaudhary, Ahmed El-Kishky, Guillaume Wenzek, Myle Ott, and Marc’Aurelio Ranzato. 2019. Facebook AI’s WAT19 Myanmar-English Translation Task Submission. In Proceedings of the 6th Workshop on Asian Translation, pages 112–122, Hong Kong, China. Association for Computational Linguistics.