Leveraging Adversarial Training in Self-Learningfor Cross-Lingual Text Classification
Table 4. Accuracy (in %) on MLDoc English code-switching data. The respective ratios of replaced words from the vocabulary and replaced word token occurrences in the English test set are given in parentheses.
['[EMPTY]', '[ITALIC] en (0%/0%)', '[ITALIC] de (16%/52% )', '[ITALIC] zh (9%32%)', '[ITALIC] es (16%/52%)']
Influence of Adversarial Perturbations. To further evaluate the effect of adversarial perturbation and straight-forwardly show that it enables robustness with respect to divergence in the test set , we conduct an additional experiment on code-switching data. As a result, the test set documents consist of a form of code-switched language, in which many words are non-English but the word order remains unchanged. We observe that the baselines have a low accuracy when faced with such codeswitching in the test set. This applies to Multilingual BERT without perturbation as well as Multilingual BERT with random perturbation. In contrast, our adversarial perturbation is significantly more effective than no or random perturbation when dealing with this data, and it does not impede the accuracy on English compared with random noise, thus improving both generalization and robustness.