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        <title>Improving Robustness of Machine Translation with Synthetic Noise</title>
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        <namePart type="given">Vaibhav</namePart>
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        <namePart type="given">Sumeet</namePart>
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    <name type="personal">
        <namePart type="given">Craig</namePart>
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    <abstract>Modern Machine Translation (MT) systems perform remarkably well on clean, in-domain text. However most of the human generated text, particularly in the realm of social media, is full of typos, slang, dialect, idiolect and other noise which can have a disastrous impact on the accuracy of MT. In this paper we propose methods to enhance the robustness of MT systems by emulating naturally occurring noise in otherwise clean data. Synthesizing noise in this manner we are ultimately able to make a vanilla MT system more resilient to naturally occurring noise, partially mitigating loss in accuracy resulting therefrom.</abstract>
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%0 Conference Proceedings
%T Improving Robustness of Machine Translation with Synthetic Noise
%A Vaibhav, Vaibhav
%A Singh, Sumeet
%A Stewart, Craig
%A Neubig, Graham
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F vaibhav-etal-2019-improving
%X Modern Machine Translation (MT) systems perform remarkably well on clean, in-domain text. However most of the human generated text, particularly in the realm of social media, is full of typos, slang, dialect, idiolect and other noise which can have a disastrous impact on the accuracy of MT. In this paper we propose methods to enhance the robustness of MT systems by emulating naturally occurring noise in otherwise clean data. Synthesizing noise in this manner we are ultimately able to make a vanilla MT system more resilient to naturally occurring noise, partially mitigating loss in accuracy resulting therefrom.
%R 10.18653/v1/N19-1190
%U https://aclanthology.org/N19-1190
%U https://doi.org/10.18653/v1/N19-1190
%P 1916-1920

Markdown (Informal)

[Improving Robustness of Machine Translation with Synthetic Noise](https://aclanthology.org/N19-1190) (Vaibhav et al., NAACL 2019)

• Improving Robustness of Machine Translation with Synthetic Noise (Vaibhav et al., NAACL 2019)

ACL

• Vaibhav Vaibhav, Sumeet Singh, Craig Stewart, and Graham Neubig. 2019. Improving Robustness of Machine Translation with Synthetic Noise. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1916–1920, Minneapolis, Minnesota. Association for Computational Linguistics.