We consider the problem of making machine translation more robust to
character-level variation at the source side, such as typos. Existing methods
achieve greater coverage by applying subword models such as byte-pair encoding
(BPE) and character-level encoders, but these methods are highly sensitive to
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 noise, as captured by frequent corrections in Wikipedia edit logs, 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.

本文提出通过引入随机合成的少量噪音来改善机器翻译中源文本字符级别波动的鲁棒性，从而提高对拼写错误等变异的覆盖率。作者表明，通过在训练时使用一些较为简单的合成噪音，可以实现对常见噪音（例如 Wikipedia 编辑日志中的修正）的鲁棒性，同时不会降低在规范文本上的表现。