TreeSwap: Data Augmentation for Machine Translation via Dependency
  Subtree Swapping
Attila Nagy Dorina Lakatos Botond Barta Judit Ács
Data augmentation methods for neural machine translation are particularly
useful when limited amount of training data is available, which is often the
case when dealing with low-resource languages. We introduce a novel
augmentation method, which generates new sentences by swapping objects and
subjects across bisentences. This is performed simultaneously based on the
dependency parse trees of the source and target sentences. We name this method
TreeSwap. Our results show that TreeSwap achieves consistent improvements over
baseline models in 4 language pairs in both directions on resource-constrained
datasets. We also explore domain-specific corpora, but find that our method
does not make significant improvements on law, medical and IT data. We report
the scores of similar augmentation methods and find that TreeSwap performs
comparably. We also analyze the generated sentences qualitatively and find that
the augmentation produces a correct translation in most cases. Our code is
available on Github.