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Modern Machine Translation (MT) systems perform consistently well on clean,in-domain text. However most human generated text, particularly in the realm ofsocial media, is full of typos, slang, dialect, idiolect and other noise whichcan have a disastrous impact on the accuracy of output translation. In thispaper we leverage the Machine Translation of Noisy Text (MTNT) dataset toenhance the robustness of MT systems by emulating naturally occurring noise inotherwise clean data. Synthesizing noise in this manner we are ultimately ableto make a vanilla MT system resilient to naturally occurring noise andpartially mitigate loss in accuracy resulting therefrom.

Improving Robustness of Machine Translation with Synthetic Noise