AdvAug: Robust Adversarial Augmentation for Neural Machine Translation
Table 2: Results on IWSLT16 English-French and WMT14 English-German translation.
['Method', 'Loss Config.', 'English-French test2013', 'English-French test2014', 'English-German newstest13', 'English-German newstest14']
English-French and English-German Translation. Our approach consistently outperforms all three baseline methods, yielding significant 3.34 and 2.27 BLEU point gains over the Transformer on the English-French and English-German translation tasks, respectively. We also conduct similar ablation studies on the translation loss. We still find that the combination of \mathcal{L}_{adv} abd \mathcal{L}_{aut} performs the best, which is consistent with the findings in the Chinese-English translation task. The substantial gains on these two translation tasks suggest the potential applicability of our approach to more language pairs.