  Data augmentation is an effective performance enhancement in neural machine
translation (NMT) by generating additional bilingual data. In this paper, we
propose a novel data augmentation enhancement strategy for neural machine
translation. Different from existing data augmentation methods which simply
choose words with the same probability across different sentences for
modification, we set sentence-specific probability for word selection by
considering their roles in sentence. We use dependency parse tree of input
sentence as an effective clue to determine selecting probability for every
words in each sentence. Our proposed method is evaluated on WMT14
English-to-German dataset and IWSLT14 German-to-English dataset. The result of
extensive experiments show our proposed syntax-aware data augmentation method
may effectively boost existing sentence-independent methods for significant
translation performance improvement.
