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.

确定性可逆数据增强（DRDA）是一种简单但有效的数据增强方法，用于神经机器翻译，通过采用确定性分段和可逆操作生成多粒度子词表示，并通过多视角技术将它们拉近，无需额外的语料库或模型变更，DRDA 在几个翻译任务中优于强基线模型（Transformer），在嘈杂、资源匮乏和跨领域数据集中表现出良好的鲁棒性，达到多达 4.3 BLEU 增益。