AdvAug: Robust Adversarial Augmentation for Neural Machine Translation
Table 5: Results on artificial noisy inputs. The column lists results for different noise fractions.
['Method', '0.00', '0.05', '0.10', '0.15']
To test robustness on noisy inputs, we follow Cheng:19 to construct a noisy data set by randomly replacing a word in each sentence of the standard validation set with a relevant alternative. The relevance between words is measured by the similarity of word embeddings. 100 noisy sentences are generated for each of them and then re-scored to pick the best one with a bidirectional language model. Our approach shows higher robustness over all baseline methods across all noise levels.