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        <title>Improving Robustness of Neural Machine Translation with Multi-task Learning</title>
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        <namePart type="given">Shuyan</namePart>
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    <name type="personal">
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    <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.</abstract>
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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
%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.