  The recent trend in industry-setting Natural Language Processing (NLP)
research has been to operate large %scale pretrained language models like BERT
under strict computational limits. While most model compression work has
focused on "distilling" a general-purpose language representation using
expensive pretraining distillation, less attention has been paid to creating
smaller task-specific language representations which, arguably, are more useful
in an industry setting. In this paper, we investigate compressing BERT- and
RoBERTa-based question answering systems by structured pruning of parameters
from the underlying transformer model. We find that an inexpensive combination
of task-specific structured pruning and task-specific distillation, without the
expense of pretraining distillation, yields highly-performing models across a
range of speed/accuracy tradeoff operating points. We start from existing
full-size models trained for SQuAD 2.0 or Natural Questions and introduce gates
that allow selected parts of transformers to be individually eliminated.
Specifically, we investigate (1) structured pruning to reduce the number of
parameters in each transformer layer, (2) applicability to both BERT- and
RoBERTa-based models, (3) applicability to both SQuAD 2.0 and Natural
Questions, and (4) combining structured pruning with distillation. We achieve a
near-doubling of inference speed with less than a 0.5 F1-point loss in short
answer accuracy on Natural Questions.
