Neural machine translation (MT) models achieve strong results across a variety of settings, but it is widely believed that they are highly sensitive to "noisy" inputs, such as spelling errors, abbreviations, and other formatting issues. In this paper, we revisit this insight in light of recent multilingual MT models and large language models (LLMs) applied to machine translation. Somewhat surprisingly, we show through controlled experiments that these models are far more robust to many kinds of noise than previous models, even when they perform similarly on clean data. This is notable because, even though LLMs have more parameters and more complex training processes than past models, none of the open ones we consider use any techniques specifically designed to encourage robustness. Next, we show that similar trends hold for social media translation experiments -- LLMs are more robust to social media text. We include an analysis of the circumstances in which source correction techniques can be used to mitigate the effects of noise. Altogether, we show that robustness to many types of noise has increased.

通过对多语种机器翻译模型和大型语言模型在噪声输入情境下的实验研究，我们发现这些模型相比以往模型对各种噪声更加稳健，尤其是在处理干净数据情况下表现相似的情况下。我们还展示了这种趋势在社交媒体翻译实验中同样存在，同时分析了源文本校正技术在减轻噪声影响方面的应用情况。综上所述，我们展示了对于多种类型噪声的稳健性有所增强。

机器翻译模型是否在不被察觉的情况下变得更加健壮了？