  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.
