  This paper introduces a new data augmentation method for neural machine
translation that can enforce stronger semantic consistency both within and
across languages. Our method is based on Conditional Masked Language Model
(CMLM) which is bi-directional and can be conditional on both left and right
context, as well as the label. We demonstrate that CMLM is a good technique for
generating context-dependent word distributions. In particular, we show that
CMLM is capable of enforcing semantic consistency by conditioning on both
source and target during substitution. In addition, to enhance diversity, we
incorporate the idea of soft word substitution for data augmentation which
replaces a word with a probabilistic distribution over the vocabulary.
Experiments on four translation datasets of different scales show that the
overall solution results in more realistic data augmentation and better
translation quality. Our approach consistently achieves the best performance in
comparison with strong and recent works and yields improvements of up to 1.90
BLEU points over the baseline.
