  Data augmentation is an effective way to diversify corpora in machine
translation, but previous methods may introduce semantic inconsistency between
original and augmented data because of irreversible operations and random
subword sampling procedures. To generate both symbolically diverse and
semantically consistent augmentation data, we propose Deterministic Reversible
Data Augmentation (DRDA), a simple but effective data augmentation method for
neural machine translation. DRDA adopts deterministic segmentations and
reversible operations to generate multi-granularity subword representations and
pulls them closer together with multi-view techniques. With no extra corpora or
model changes required, DRDA outperforms strong baselines on several
translation tasks with a clear margin (up to 4.3 BLEU gain over Transformer)
and exhibits good robustness in noisy, low-resource, and cross-domain datasets.
