  Structured pruning methods have proven effective in reducing the model size
and accelerating inference speed in various network architectures such as
Transformers. Despite the versatility of encoder-decoder models in numerous NLP
tasks, the structured pruning methods on such models are relatively less
explored compared to encoder-only models. In this study, we investigate the
behavior of the structured pruning of the encoder-decoder models in the
decoupled pruning perspective of the encoder and decoder component,
respectively. Our findings highlight two insights: (1) the number of decoder
layers is the dominant factor of inference speed, and (2) low sparsity in the
pruned encoder network enhances generation quality. Motivated by these
findings, we propose a simple and effective framework, NASH, that narrows the
encoder and shortens the decoder networks of encoder-decoder models. Extensive
experiments on diverse generation and inference tasks validate the
effectiveness of our method in both speedup and output quality.
