LightMBERT: A Simple Yet Effective Method for Multilingual BERT Distillation
Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Dong Chen, Linlin Li, Fang Wang, Liu, Qun · arXiv (Cornell University) · 2021
The multilingual pre-trained language models (e.g, mBERT, XLM and XLM-R) have shown impressive performance on cross-lingual natural language understanding tasks. However, these models are computationally intensive and difficult to be deployed on resource-restricted devices. In this paper, we propose a simple yet effective distillation method (LightMBERT) for transferring the cross-lingual generalization ability of the multilingual BERT to a small student model. The experiment results empirically demonstrate the efficiency and effectiveness of LightMBERT, which is significantly better than the baselines and performs comparable to the teacher mBERT.