Before revision: The vanilla Transformer conducts a fixed number of computations over all words in a sentence, irrespective of whether they are easy or difficult to learn. In terms of both computational efficiency and ease of learning, it is preferable to dynamically vary the numbers of computations according to the hardness of the input words.
After revision: Depth-adaptive neural networks can dynamically adjust depths according to the hardness of the input words.