Adversarial Subword Regularization for Robust Neural Machine Translation
Table 4: BLEU scores on the synthetic dataset of typos. The column lists results for different noise fractions.
['[BOLD] Method', '[BOLD] 0.1', '[BOLD] 0.2', '[BOLD] 0.3', '[BOLD] 0.4', '[BOLD] 0.5']
As the noise fraction increases, our method shows its high robustness compared to the baseline models maximally improving 10.8 BLEU scores over Base, and 7.5 over SR. This verifies that our method effectively improves the robustness of the NMT models against segmentation errors from typos.