Before revision:  Results on the classic text classification task ( 24 datasets in various sizes and domains ) show that our approaches achieve superior performance while preserving higher efficiency in computation over the vanilla Transformer and previous depth-adaptive models. More importantly, our approaches lead to more robust depth-adaptive Transformer models with better interpretability of the depth distribution .
After revision:  Results on the classic text classification task ( 24 datasets in various sizes and domains ) show that our approaches achieve superior performance while preserving higher efficiency in computation over the vanilla Transformer and previous depth-adaptive approaches .