Leveraging Adversarial Training in Self-Learningfor Cross-Lingual Text Classification
Table 1. Accuracy (in %) on MLDoc experiments. Bold denotes the best on cross-lingual transfer.
['Approach', '[ITALIC] en', '[ITALIC] de', '[ITALIC] zh', '[ITALIC] es', '[ITALIC] fr', '[ITALIC] it', '[ITALIC] ja', '[ITALIC] ru']
Cross-lingual Document Classification. Our MLDoc experiments compare our approach against several strong baselines. Schwenk et al. Artetxe et al. pretrain a massively multilingual sequence-to-sequence neural MT model, invoking its encoder as a multilingual text representation used for fine-tuning on downstream tasks. Keung et al. We also considered Multilingual BERT and itself with self-learning and adversarial training respectively as our baselines. Additionally, we compare Multilingual BERT with self-learning using unlabeled data as investigated by Dong et al. Beyond this, our full framework further outperforms all baselines across 7 languages, including for phylogenetically unrelated languages.