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.

我们提出了第一款可在推理过程中跳过和跳转文本的神经速读模型，名为Structural-Jump-LSTM，它由标准LSTM和两个代理组成，分别能够在阅读时跳过单词和利用标点结构来跳转。和其他五个最先进的神经阅读模型相比，我们的模型的浮点运算（FLOP）减少最大，速度最快，同时保持了与一个传统的LSTM相同的准确性甚至更好。

结构跳跃LSTM的神经速读