  Many efforts have been made to facilitate natural language processing tasks
with pre-trained language models (LMs), and brought significant improvements to
various applications. To fully leverage the nearly unlimited corpora and
capture linguistic information of multifarious levels, large-size LMs are
required; but for a specific task, only parts of these information are useful.
Such large-sized LMs, even in the inference stage, may cause heavy computation
workloads, making them too time-consuming for large-scale applications. Here we
propose to compress bulky LMs while preserving useful information with regard
to a specific task. As different layers of the model keep different
information, we develop a layer selection method for model pruning using
sparsity-inducing regularization. By introducing the dense connectivity, we can
detach any layer without affecting others, and stretch shallow and wide LMs to
be deep and narrow. In model training, LMs are learned with layer-wise dropouts
for better robustness. Experiments on two benchmark datasets demonstrate the
effectiveness of our method.
