Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER

Phillip Keung, Yichao Lu, Vikas Bhardwaj


Abstract
Contextual word embeddings (e.g. GPT, BERT, ELMo, etc.) have demonstrated state-of-the-art performance on various NLP tasks. Recent work with the multilingual version of BERT has shown that the model performs surprisingly well in cross-lingual settings, even when only labeled English data is used to finetune the model. We improve upon multilingual BERT’s zero-resource cross-lingual performance via adversarial learning. We report the magnitude of the improvement on the multilingual MLDoc text classification and CoNLL 2002/2003 named entity recognition tasks. Furthermore, we show that language-adversarial training encourages BERT to align the embeddings of English documents and their translations, which may be the cause of the observed performance gains.
Anthology ID:
D19-1138
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1355–1360
Language:
URL:
https://aclanthology.org/D19-1138
DOI:
10.18653/v1/D19-1138
Bibkey:
Cite (ACL):
Phillip Keung, Yichao Lu, and Vikas Bhardwaj. 2019. Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1355–1360, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER (Keung et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1138.pdf
Data
MLDoc