When translating from a language that does not morphologically mark information such as gender and number into a language that does, translation systems must "guess" this missing information, often leading to incorrect translations in the given context. We propose a black-box approach for injecting the missing information to a pre-trained neural machine translation system, allowing to control the morphological variations in the generated translations without changing the underlying model or training data. We evaluate our method on an English to Hebrew translation task, and show that it is effective in injecting the gender and number information and that supplying the correct information improves the translation accuracy in up to 2.3 BLEU on a female-speaker test set for a state-of-the-art online black-box system. Finally, we perform a fine-grained syntactic analysis of the generated translations that shows the effectiveness of our method.

提出使用黑盒方法向预训练的神经机器翻译系统注入缺失的语言特征，以实现控制生成的翻译的形态变化，我们对英文到希伯来文的翻译任务进行了评估，证明了这种方法是有效的，并且注入正确信息可以将翻译准确率提高高达2.3 BLEU。最后，我们进行了生成翻译的详细句法分析，证明了我们的方法的有效性。

使用黑盒上下文注入填补神经机器翻译中的性别和数量差距