Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER (D19-1)
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| Challenge: | Contextual word embeddings have demonstrated state-of-the-art performance on various NLP tasks. |
| Approach: | They propose to use adversarial learning to improve upon multilingual BERT's zero-resource cross-lingual performance by aligning embeddings of English documents and their translations. |
| Outcome: | The multilingual version of BERT performs surprisingly well in cross-lingual settings, even when only labeled English data is used to finetune the model. |
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