  The sizes of pretrained language models make them challenging and expensive
to use when there are multiple desired downstream tasks. In this work, we adopt
recent strategies for model pruning during finetuning to explore the question
of whether it is possible to prune a single encoder so that it can be used for
multiple tasks. We allocate a fixed parameter budget and compare pruning a
single model with a multitask objective against the best ensemble of
single-task models. We find that under two pruning strategies (element-wise and
rank pruning), the approach with the multitask objective outperforms training
models separately when averaged across all tasks, and it is competitive on each
individual one. Additional analysis finds that using a multitask objective
during pruning can also be an effective method for reducing model sizes for
low-resource tasks.
