  Existing data augmentation approaches for neural machine translation (NMT)
have predominantly relied on back-translating in-domain (IND) monolingual
corpora. These methods suffer from issues associated with a domain information
gap, which leads to translation errors for low frequency and out-of-vocabulary
terminology. This paper proposes a dictionary-based data augmentation (DDA)
method for cross-domain NMT. DDA synthesizes a domain-specific dictionary with
general domain corpora to automatically generate a large-scale pseudo-IND
parallel corpus. The generated pseudo-IND data can be used to enhance a general
domain trained baseline. The experiments show that the DDA-enhanced NMT models
demonstrate consistent significant improvements, outperforming the baseline
models by 3.75-11.53 BLEU. The proposed method is also able to further improve
the performance of the back-translation based and IND-finetuned NMT models. The
improvement is associated with the enhanced domain coverage produced by DDA.
