@inproceedings{103581dc76c54cd3bbec96b03122fbdb,
title = "Can Monolingual Pretrained Models Help Cross-Lingual Classification?",
abstract = "Multilingual pretrained language models (such as multilingual BERT) have achieved impressive results for cross-lingual transfer. However, due to the constant model capacity, multilingual pre-training usually lags behind the monolingual competitors. In this work, we present two approaches to improve zero-shot cross-lingual classification, by transferring the knowledge from monolingual pretrained models to multilingual ones. Experimental results on two cross-lingual classification benchmarks show that our methods outperform vanilla multilingual fine-tuning.",
author = "Zewen Chi and Li Dong and Furu Wei and Mao, \{Xian Ling\} and Heyan Huang",
note = "Publisher Copyright: {\textcopyright} 2020 Association for Computational Linguistics.; 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, AACL-IJCNLP 2020 ; Conference date: 04-12-2020 Through 07-12-2020",
year = "2020",
doi = "10.18653/v1/2020.aacl-main.2",
language = "English",
series = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, AACL-IJCNLP 2020",
publisher = "Association for Computational Linguistics (ACL)",
pages = "12--17",
editor = "Kam-Fai Wong and Kevin Knight and Hua Wu",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, AACL-IJCNLP 2020",
address = "United States",
}