Adversarial Subword Regularization for Robust Neural Machine Translation
Table 2: BLEU scores on the main results. Bold indicates the best score and all scores whose difference from the best is not statistically significant (with p-value of 0.05). (Statistical significance is computed via bootstrapping (Koehn, 2004))
['[BOLD] Lang Pair', '[BOLD] Base', '[BOLD] SR', '[BOLD] AdvSR']
Our method improves over the Base and the SR consistently. This shows that diversely exposing various segmentations is more effective than making the NMT models observe limited sets of segmentation candidates in the low-resource setting. Specifically, AdvSR maximally improves 2.2 BLEU over SR and 3.5 BLEU over Base in the English to Czech dataset. We assume that the large gains are due to the linguistic characteristic of Czech, which is morphologically rich.