The training data used in NMT is rarely controlled with respect to specific attributes, such as word casing or gender, which can cause errors in translations. We argue that predicting the target word and attributes simultaneously is an effective way to ensure that translations are more faithful to the training data distribution with respect to these attributes. Experimental results on two tasks, uppercased input translation and gender prediction, show that this strategy helps mirror the training data distribution in testing. It also facilitates data augmentation on the task of uppercased input translation.

翻译：国家管理工具使用的培训数据很少在具体属性方面受到控制,如字外壳或性别等,这可能造成翻译错误。我们争辩说,同时预测目标字和属性是确保翻译更忠实于与这些属性有关的培训数据分布的有效方式。两项任务,即大写输入翻译和性别预测的实验结果显示,这一战略有助于在测试中反映培训数据分布。它也有助于增加关于大写输入翻译任务的数据。