  Exposing diverse subword segmentations to neural machine translation (NMT)
models often improves the robustness of machine translation as NMT models can
experience various subword candidates. However, the diversification of subword
segmentations mostly relies on the pre-trained subword language models from
which erroneous segmentations of unseen words are less likely to be sampled. In
this paper, we present adversarial subword regularization (ADVSR) to study
whether gradient signals during training can be a substitute criterion for
exposing diverse subword segmentations. We experimentally show that our
model-based adversarial samples effectively encourage NMT models to be less
sensitive to segmentation errors and improve the performance of NMT models in
low-resource and out-domain datasets.
