  Subword units are an effective way to alleviate the open vocabulary problems
in neural machine translation (NMT). While sentences are usually converted into
unique subword sequences, subword segmentation is potentially ambiguous and
multiple segmentations are possible even with the same vocabulary. The question
addressed in this paper is whether it is possible to harness the segmentation
ambiguity as a noise to improve the robustness of NMT. We present a simple
regularization method, subword regularization, which trains the model with
multiple subword segmentations probabilistically sampled during training. In
addition, for better subword sampling, we propose a new subword segmentation
algorithm based on a unigram language model. We experiment with multiple
corpora and report consistent improvements especially on low resource and
out-of-domain settings.
