  Pre-trained language models (PLMs) have achieved great success in natural
language processing. Most of PLMs follow the default setting of architecture
hyper-parameters (e.g., the hidden dimension is a quarter of the intermediate
dimension in feed-forward sub-networks) in BERT (Devlin et al., 2019). Few
studies have been conducted to explore the design of architecture
hyper-parameters in BERT, especially for the more efficient PLMs with tiny
sizes, which are essential for practical deployment on resource-constrained
devices. In this paper, we adopt the one-shot Neural Architecture Search (NAS)
to automatically search architecture hyper-parameters. Specifically, we
carefully design the techniques of one-shot learning and the search space to
provide an adaptive and efficient development way of tiny PLMs for various
latency constraints. We name our method AutoTinyBERT and evaluate its
effectiveness on the GLUE and SQuAD benchmarks. The extensive experiments show
that our method outperforms both the SOTA search-based baseline (NAS-BERT) and
the SOTA distillation-based methods (such as DistilBERT, TinyBERT, MiniLM and
MobileBERT). In addition, based on the obtained architectures, we propose a
more efficient development method that is even faster than the development of a
single PLM.
