Recent advances in recurrent neural nets (RNNs) have shown much promise in many applications in natural language processing. For most of these tasks, such as sentiment analysis of customer reviews, a recurrent neural net model parses the entire review before forming a decision. We argue that reading the entire input is not always necessary in practice, since a lot of reviews are often easy to classify, i.e., a decision can be formed after reading some crucial sentences or words in the provided text. In this paper, we present an approach of fast reading for text classification. Inspired by several well-known human reading techniques, our approach implements an intelligent recurrent agent which evaluates the importance of the current snippet in order to decide whether to make a prediction, or to skip some texts, or to re-read part of the sentence. Our agent uses an RNN module to encode information from the past and the current tokens, and applies a policy module to form decisions. With an end-to-end training algorithm based on policy gradient, we train and test our agent on several text classification datasets and achieve both higher efficiency and better accuracy compared to previous approaches. 
 Recurrent neural nets (RNNs), including GRU nets BID6 and LSTM nets BID12 , have been increasingly applied to many problems in natural language processing. Most of the problems can be divided into two categories: sequence to sequence (seq2seq) tasks BID29 ) (e.g., language modeling BID2 BID20 , machine translation BID13 , conversational/dialogue modeling BID26 , question answering BID11 BID17 , and document summarization BID21 ); and the classification tasks (e.g., part-of-speech tagging BID23 , chunking, named entity recognition BID7 , sentimental analysis BID28 , and document classification BID14 BID25 ). To solve these problems, models often need to read every token or word of the text from beginning to the end, which is necessary for most seq2seq problems. However, for classification problems, we do not have to treat each individual word equally, since certain words or chunks are more relevant to the classification task at hand. For instance, for sentiment analysis it is sufficient to read the first half of a review like "this movie is amazing" or "it is the best I have ever seen," to provide an answer even without reading the rest of the review. In other cases, we may want to skip or skim some text without carefully checking it. For example, sentences such as "it's worth to try" are usually more important than irrelevant text such as "we got here while it's still raining outside" or "I visited on Saturday." On the other hand, sometimes, we want to re-read some sentences to figure out the actual hidden message of the text. All of these techniques enable us to achieve fast and accurate reading. Similarly, we expect RNN models to intelligently determine the importance or the relevance of the current sentence in order to decide whether to make a prediction, whether to skip some texts, or whether to re-read the current sentence.In this paper, we aim to augment existing RNN models by introducing efficient partial reading for classification, while maintaining a higher or comparable accuracy compared to reading the full text.To do so, we introduce a recurrent agent which uses an RNN module to encode information from the past and the current tokens, and applies a policy module to decide what token to read next (e.g., rereading the current token, reading the next one, or skipping the next few tokens) or whether the model should stop reading and form a decision. To encourage fast and accurate reading, we incorporate both classification accuracy and the computational cost as a reward function to score classification or other actions made by the agent during reading. We expect that our agent will be able to achieve fast reading for classification with both high computational efficiency and good classification performance. To train this model, we develop an end-to-end approach based on the policy gradient method which backpropagates the reward signal into both the policy module (also including the classification policy) and the recurrent encoder.We evaluate our approach on four different sentiment analysis and document topic classification datasets. By comparing to the standard RNN models and a recent LSTM-skip model which implements a skip action BID33 , we find that our approach achieves both higher efficiency and better accuracy. We develop an end-to-end trainable approach for skimming, rereading and early stopping applicable to classification tasks. By mimicking human fast reading, we introduce a policy module to decide what token to read next (e.g., rereading the current token, reading the next one, or skipping the next few tokens) or whether the model should stop reading and form a decision. To encourage fast and accurate reading, we incorporate both classification accuracy and the computational cost as a reward function to score classification or other actions made by the agent during reading. An endto-end training algorithm based on the policy gradient method backpropagates the reward signal into both the policy module (also including the classification policy) and the recurrent encoder. We demonstrate the efficacy of the proposed approach on four different datasets and demonstrate improvements for both accuracy and computational performance.
We develop an end-to-end trainable approach for skimming, rereading and early stopping applicable to classification tasks. 
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