Improving machine translation (MT) systems with translation memories (TMs) is of great interest to practitioners in the MT community. However, previous approaches require either a significant update of the model architecture and/or additional training efforts to make the models well-behaved when TMs are taken as additional input. In this paper, we present a simple but effective method to introduce TMs into neural machine translation (NMT) systems. Specifically, we treat TMs as prompts to the NMT model at test time, but leave the training process unchanged. The result is a slight update of an existing NMT system, which can be implemented in a few hours by anyone who is familiar with NMT. Experimental results on several datasets demonstrate that our system significantly outperforms strong baselines.

翻译：利用翻译记忆库（TM）改进机器翻译（MT）系统是机器翻译社区从业者高度关注的问题。然而，以往的方法要求对模型架构进行显著更新，和/或需要额外的训练工作才能使模型在将翻译记忆库作为额外输入时表现良好。本文提出了一种简单而有效的方法，将翻译记忆库引入神经机器翻译（NMT）系统。具体而言，我们在测试时将翻译记忆库作为神经机器翻译模型的提示，而训练过程保持不变。结果仅需对现有神经机器翻译系统进行微小调整，任何熟悉神经机器翻译的人员都能在数小时内完成实现。在多个数据集上的实验结果表明，我们的系统显著优于强基线模型。