Data Augmentation for Machine Translation via Dependency Subtree
  Swapping
Attila Nagy Dorina Petra Lakatos Botond Barta Patrick Nanys Judit Ács
We present a generic framework for data augmentation via dependency subtree
swapping that is applicable to machine translation. We extract corresponding
subtrees from the dependency parse trees of the source and target sentences and
swap these across bisentences to create augmented samples. We perform thorough
filtering based on graphbased similarities of the dependency trees and
additional heuristics to ensure that extracted subtrees correspond to the same
meaning. We conduct resource-constrained experiments on 4 language pairs in
both directions using the IWSLT text translation datasets and the Hunglish2
corpus. The results demonstrate consistent improvements in BLEU score over our
baseline models in 3 out of 4 language pairs. Our code is available on GitHub.