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We consider the problem of making machine translation more robust tocharacter-level variation at the source side, such as typos. Existing methodsachieve greater coverage by applying subword models such as byte-pair encoding(BPE) and character-level encoders, but these methods are highly sensitive tospelling mistakes. We show how training on a mild amount of random syntheticnoise can dramatically improve robustness to these variations, withoutdiminishing performance on clean text. We focus on translation performance onnatural noise, as captured by frequent corrections in Wikipedia edit logs, andshow that robustness to such noise can be achieved using a balanced diet ofsimple synthetic noises at training time, without access to the natural noisedata or distribution.

Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation