Neural networks are deployed widely in natural language processing tasks on the industrial scale, and perhaps the most often they are used as compounds of automatic machine translation systems. In this work, we present a simple approach to fool state-of-the-art machine translation tools in the task of translation from Russian to English and vice versa. Using a novel black-box gradient-free tensor-based optimizer, we show that many online translation tools, such as Google, DeepL, and Yandex, may both produce wrong or offensive translations for nonsensical adversarial input queries and refuse to translate seemingly benign input phrases. This vulnerability may interfere with understanding a new language and simply worsen the user's experience while using machine translation systems, and, hence, additional improvements of these tools are required to establish better translation.

翻译：神经网络被广泛应用于工业规模的各类自然语言处理任务中，而自动化机器翻译系统或许是其中最常使用神经网络的场景。本研究提出了一种简单方法，可欺骗俄译英及英译俄任务中最先进的机器翻译工具。通过采用新型黑盒无梯度张量优化器，我们证明谷歌、DeepL和Yandex等众多在线翻译工具，既可能对无意义的对抗性输入查询生成错误或冒犯性译文，也可能拒绝翻译看似良性的输入短语。这种脆弱性可能会干扰用户理解新语言，并简单恶化机器翻译系统的使用体验，因此需对这些工具进行额外改进以建立更优质的翻译能力。