The pre-trained language models like BERT, though powerful in many natural language processing tasks, are both computation and memory expensive. To alleviate this problem, one approach is to compress them for specific tasks before deployment. However, recent works on BERT compression usually compress the large BERT model to a fixed smaller size, and can not fully satisfy the requirements of different edge devices with various hardware performances. In this paper, we propose a novel dynamic BERT model (abbreviated as DynaBERT), which can flexibly adjust the size and latency by selecting adaptive width and depth. The training process of DynaBERT includes first training a width-adaptive BERT and then allowing both adaptive width and depth, by distilling knowledge from the full-sized model to small sub-networks. Network rewiring is also used to keep the more important attention heads and neurons shared by more sub-networks. Comprehensive experiments under various efficiency constraints demonstrate that our proposed dynamic BERT (or RoBERTa) at its largest size has comparable performance as BERT-base (or RoBERTa-base), while at smaller widths and depths consistently outperforms existing BERT compression methods.

Additional Feedback: Random things: - Table 1 is a bit overloaded and difficult to parse. Also I'm not sure which row and column are m_w vs m_d. Can you present this differently with lines corresponding to the base models? Related Work: There's a little bit of discussion in the first half of paragraph 2 of the introduction, but no comprehensive addressing of how your work sits in context to the work already out there. Including work that talks about the capacity of large language models, what they can and can't do would be important here, how more layers/parameters help language models in general (Jawahar et al 2019; What does BERT learn about the structure of language?, Jozefowicz et al 2016 Exploring the limits of language modeling, Melis et al 2017 On the State of the Art of Evaluation in Neural Language Models, Subramani et al 2019 Can Unconditional Language Models Recover Arbitrary Sentences?).

The pre-trained language models like BERT, though powerful in many natural language processing tasks, are both computation and memory expensive. To alleviate this problem, one approach is to compress them for specific tasks before deployment. However, recent works on BERT compression usually compress the large BERT model to a fixed smaller size, and can not fully satisfy the requirements of different edge devices with various hardware performances. In this paper, we propose a novel dynamic BERT model (abbreviated as DynaBERT), which can flexibly adjust the size and latency by selecting adaptive width and depth. The training process of DynaBERT includes first training a width-adaptive BERT and then allowing both adaptive width and depth, by distilling knowledge from the full-sized model to small sub-networks. Network rewiring is also used to keep the more important attention heads and neurons shared by more sub-networks.