Recurrent neural networks (RNNs) can model natural language by sequentially ''reading'' input tokens and outputting a distributed representation of each token. Due to the sequential nature of RNNs, inference time is linearly dependent on the input length, and all inputs are read regardless of their importance. Efforts to speed up this inference, known as ''neural speed reading'', either ignore or skim over part of the input. We present Structural-Jump-LSTM: the first neural speed reading model to both skip and jump text during inference. The model consists of a standard LSTM and two agents: one capable of skipping single words when reading, and one capable of exploiting punctuation structure (sub-sentence separators (,:), sentence end symbols (.!?), or end of text markers) to jump ahead after reading a word.
 A comprehensive experimental evaluation of our model against all five state-of-the-art neural reading models shows that 
 Structural-Jump-LSTM achieves the best overall floating point operations (FLOP) reduction (hence is faster), while keeping the same accuracy or even improving it compared to a vanilla LSTM that reads the whole text.
We propose a new model for neural speed reading that utilizes the inherent punctuation structure of a text to define effective jumping and skipping behavior. The paper proposes a Structural-Jump-LSTM model to speed up machine reading with two agents instead of one Proposes a novel model for neural speed reading in which the new reader has the ability to skip a word or sequence of words. The paper proposes a fast-reading method using skip and jump actions, showing that the proposed method is as accurate as LSTM but uses much less computation.