Improving Robustness of Neural Machine Translation with Multi-task Learning
Shuyan Zhou, Xiangkai Zeng, Yingqi Zhou, Antonios Anastasopoulos, Graham Neubig
Abstract
While neural machine translation (NMT) achieves remarkable performance on clean, in-domain text, performance is known to degrade drastically when facing text which is full of typos, grammatical errors and other varieties of noise. In this work, we propose a multi-task learning algorithm for transformer-based MT systems that is more resilient to this noise. We describe our submission to the WMT 2019 Robustness shared task based on this method. Our model achieves a BLEU score of 32.8 on the shared task French to English dataset, which is 7.1 BLEU points higher than the baseline vanilla transformer trained with clean text.- Anthology ID:
- W19-5368
- Volume:
- Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Marco Turchi, Karin Verspoor
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 565–571
- Language:
- URL:
- https://aclanthology.org/W19-5368
- DOI:
- 10.18653/v1/W19-5368
- Bibkey:
- Cite (ACL):
- Shuyan Zhou, Xiangkai Zeng, Yingqi Zhou, Antonios Anastasopoulos, and Graham Neubig. 2019. Improving Robustness of Neural Machine Translation with Multi-task Learning. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 565–571, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Improving Robustness of Neural Machine Translation with Multi-task Learning (Zhou et al., WMT 2019)
- Copy Citation:
- PDF:
- https://aclanthology.org/W19-5368.pdf
- Code
- shuyanzhou/multitask_transformer
- Data
- MTNT
Export citation
@inproceedings{zhou-etal-2019-improving,
title = "Improving Robustness of Neural Machine Translation with Multi-task Learning",
author = "Zhou, Shuyan and
Zeng, Xiangkai and
Zhou, Yingqi and
Anastasopoulos, Antonios and
Neubig, Graham",
editor = "Bojar, Ond{\v{r}}ej and
Chatterjee, Rajen and
Federmann, Christian and
Fishel, Mark and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Monz, Christof and
Negri, Matteo and
N{\'e}v{\'e}ol, Aur{\'e}lie and
Neves, Mariana and
Post, Matt and
Turchi, Marco and
Verspoor, Karin",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5368",
doi = "10.18653/v1/W19-5368",
pages = "565--571",
abstract = "While neural machine translation (NMT) achieves remarkable performance on clean, in-domain text, performance is known to degrade drastically when facing text which is full of typos, grammatical errors and other varieties of noise. In this work, we propose a multi-task learning algorithm for transformer-based MT systems that is more resilient to this noise. We describe our submission to the WMT 2019 Robustness shared task based on this method. Our model achieves a BLEU score of 32.8 on the shared task French to English dataset, which is 7.1 BLEU points higher than the baseline vanilla transformer trained with clean text.",
}
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%0 Conference Proceedings %T Improving Robustness of Neural Machine Translation with Multi-task Learning %A Zhou, Shuyan %A Zeng, Xiangkai %A Zhou, Yingqi %A Anastasopoulos, Antonios %A Neubig, Graham %Y Bojar, Ondřej %Y Chatterjee, Rajen %Y Federmann, Christian %Y Fishel, Mark %Y Graham, Yvette %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Martins, André %Y Monz, Christof %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Post, Matt %Y Turchi, Marco %Y Verspoor, Karin %S Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1) %D 2019 %8 August %I Association for Computational Linguistics %C Florence, Italy %F zhou-etal-2019-improving %X While neural machine translation (NMT) achieves remarkable performance on clean, in-domain text, performance is known to degrade drastically when facing text which is full of typos, grammatical errors and other varieties of noise. In this work, we propose a multi-task learning algorithm for transformer-based MT systems that is more resilient to this noise. We describe our submission to the WMT 2019 Robustness shared task based on this method. Our model achieves a BLEU score of 32.8 on the shared task French to English dataset, which is 7.1 BLEU points higher than the baseline vanilla transformer trained with clean text. %R 10.18653/v1/W19-5368 %U https://aclanthology.org/W19-5368 %U https://doi.org/10.18653/v1/W19-5368 %P 565-571
Markdown (Informal)
[Improving Robustness of Neural Machine Translation with Multi-task Learning](https://aclanthology.org/W19-5368) (Zhou et al., WMT 2019)
- Improving Robustness of Neural Machine Translation with Multi-task Learning (Zhou et al., WMT 2019)
ACL
- Shuyan Zhou, Xiangkai Zeng, Yingqi Zhou, Antonios Anastasopoulos, and Graham Neubig. 2019. Improving Robustness of Neural Machine Translation with Multi-task Learning. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 565–571, Florence, Italy. Association for Computational Linguistics.