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        <title>Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation</title>
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    <name type="personal">
        <namePart type="given">Vladimir</namePart>
        <namePart type="family">Karpukhin</namePart>
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    <name type="personal">
        <namePart type="given">Omer</namePart>
        <namePart type="family">Levy</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Marjan</namePart>
        <namePart type="family">Ghazvininejad</namePart>
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            <title>Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)</title>
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            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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        <name type="personal">
            <namePart type="given">Alan</namePart>
            <namePart type="family">Ritter</namePart>
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            <namePart type="given">Tim</namePart>
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            <namePart type="given">Afshin</namePart>
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    <abstract>Contemporary machine translation systems achieve greater coverage by applying subword models such as BPE and character-level CNNs, but these methods are highly sensitive to orthographical variations such as spelling mistakes. We show how training on a mild amount of random synthetic noise can dramatically improve robustness to these variations, without diminishing performance on clean text. We focus on translation performance on natural typos, and show that robustness to such noise can be achieved using a balanced diet of simple synthetic noises at training time, without access to the natural noise data or distribution.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5506</identifier>
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%0 Conference Proceedings
%T Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation
%A Karpukhin, Vladimir
%A Levy, Omer
%A Eisenstein, Jacob
%A Ghazvininejad, Marjan
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F karpukhin-etal-2019-training
%X Contemporary machine translation systems achieve greater coverage by applying subword models such as BPE and character-level CNNs, but these methods are highly sensitive to orthographical variations such as spelling mistakes. We show how training on a mild amount of random synthetic noise can dramatically improve robustness to these variations, without diminishing performance on clean text. We focus on translation performance on natural typos, and show that robustness to such noise can be achieved using a balanced diet of simple synthetic noises at training time, without access to the natural noise data or distribution.
%R 10.18653/v1/D19-5506
%U https://aclanthology.org/D19-5506
%U https://doi.org/10.18653/v1/D19-5506
%P 42-47

Markdown (Informal)

[Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation](https://aclanthology.org/D19-5506) (Karpukhin et al., WNUT 2019)

• Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation (Karpukhin et al., WNUT 2019)

ACL

• Vladimir Karpukhin, Omer Levy, Jacob Eisenstein, and Marjan Ghazvininejad. 2019. Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 42–47, Hong Kong, China. Association for Computational Linguistics.