  Natural Language Processing (NLP) has recently achieved great success by
using huge pre-trained models with hundreds of millions of parameters. However,
these models suffer from heavy model sizes and high latency such that they
cannot be deployed to resource-limited mobile devices. In this paper, we
propose MobileBERT for compressing and accelerating the popular BERT model.
Like the original BERT, MobileBERT is task-agnostic, that is, it can be
generically applied to various downstream NLP tasks via simple fine-tuning.
Basically, MobileBERT is a thin version of BERT_LARGE, while equipped with
bottleneck structures and a carefully designed balance between self-attentions
and feed-forward networks. To train MobileBERT, we first train a specially
designed teacher model, an inverted-bottleneck incorporated BERT_LARGE model.
Then, we conduct knowledge transfer from this teacher to MobileBERT. Empirical
studies show that MobileBERT is 4.3x smaller and 5.5x faster than BERT_BASE
while achieving competitive results on well-known benchmarks. On the natural
language inference tasks of GLUE, MobileBERT achieves a GLUEscore o 77.7 (0.6
lower than BERT_BASE), and 62 ms latency on a Pixel 4 phone. On the SQuAD
v1.1/v2.0 question answering task, MobileBERT achieves a dev F1 score of
90.0/79.2 (1.5/2.1 higher than BERT_BASE).

Computation and Language;Machine Learning