Can Monolingual Pretrained Models Help Cross-Lingual Classification?

Zewen Chi1, Li Dong2, Furu Wei2, Xianling Mao1, Heyan Huang1
1Beijing Institute of Technology, 2Microsoft Research


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.