  Neural Machine Translation (NMT) models are sensitive to small perturbations
in the input. Robustness to such perturbations is typically measured using
translation quality metrics such as BLEU on the noisy input. This paper
proposes additional metrics which measure the relative degradation and changes
in translation when small perturbations are added to the input. We focus on a
class of models employing subword regularization to address robustness and
perform extensive evaluations of these models using the robustness measures
proposed. Results show that our proposed metrics reveal a clear trend of
improved robustness to perturbations when subword regularization methods are
used.
