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 NMT 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.
Song et al. REF , in turn, produce code-switched training data by replacing source phrases with their target translations and use a pointer network to enhance their copy into the target side.
Code-Switching for Enhancing NMT with Pre-Specified Translation
ArXiv
ArXiv
Computer Science