In cross-lingual text classification, one seeks to exploit labeled data from
one language to train a text classification model that can then be applied to a
completely different language. Recent multilingual representation models have
made it much easier to achieve this. Still, there may still be subtle
differences between languages that are neglected when doing so. To address
this, we present a semi-supervised adversarial training process that minimizes
the maximal loss for label-preserving input perturbations. The resulting model
then serves as a teacher to induce labels for unlabeled target language samples
that can be used during further adversarial training, allowing us to gradually
adapt our model to the target language. Compared with a number of strong
baselines, we observe significant gains in effectiveness on document and intent
classification for a diverse set of languages.

本研究提出了一种半监督敌对训练过程，通过最小化标签保留输入扰动的最大损失来改进跨语言文本分类，进而为目标语言样本诱导标签以进一步适应模型。与多个强基线比较，我们观察到了在各种语言的文档和意图分类上的显著增益。