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- W2949251895 abstract "Modern Machine Translation (MT) systems perform consistently well on clean, in-domain text. However most 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 output translation. In this paper we leverage the Machine Translation of Noisy Text (MTNT) dataset 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 resilient to naturally occurring noise and partially mitigate loss in accuracy resulting therefrom." @default.
- W2949251895 created "2019-06-27" @default.
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- W2949251895 date "2019-02-25" @default.
- W2949251895 modified "2023-09-26" @default.
- W2949251895 title "Improving Robustness of Machine Translation with Synthetic Noise" @default.
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- W2949251895 doi "https://doi.org/10.48550/arxiv.1902.09508" @default.
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