  Leveraging user-provided translation to constrain NMT has practical
significance. Existing methods can be classified into two main categories,
namely the use of placeholder tags for lexicon words and the use of hard
constraints during decoding. Both methods can hurt translation fidelity for
various reasons. We investigate a data augmentation method, making
code-switched training data by replacing source phrases with their target
translations. Our method does not change the MNT model or decoding algorithm,
allowing the model to learn lexicon translations by copying source-side target
words. Extensive experiments show that our method achieves consistent
improvements over existing approaches, improving translation of constrained
words without hurting unconstrained words.
