@inproceedings{xu-etal-2020-bert,
    title = "{BERT}-of-Theseus: Compressing {BERT} by Progressive Module Replacing",
    author = "Xu, Canwen  and
      Zhou, Wangchunshu  and
      Ge, Tao  and
      Wei, Furu  and
      Zhou, Ming",
    editor = "Webber, Bonnie  and
      Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.emnlp-main.633/",
    doi = "10.18653/v1/2020.emnlp-main.633",
    pages = "7859--7869",
    abstract = "In this paper, we propose a novel model compression approach to effectively compress BERT by progressive module replacing. Our approach first divides the original BERT into several modules and builds their compact substitutes. Then, we randomly replace the original modules with their substitutes to train the compact modules to mimic the behavior of the original modules. We progressively increase the probability of replacement through the training. In this way, our approach brings a deeper level of interaction between the original and compact models. Compared to the previous knowledge distillation approaches for BERT compression, our approach does not introduce any additional loss function. Our approach outperforms existing knowledge distillation approaches on GLUE benchmark, showing a new perspective of model compression."
}