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.
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 (up to 7x) while preserving high accuracy. Moreover, efficiency and robustness are significantly improved when compared with other depth-adaptive models.