Fast and Accurate Text Classification: Skimming, Rereading and Early Stopping
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.
 The paper present a model for fast reading for text classification with mechanisms that allow the model to reread, skip words, or classify early before reading the entire review. The model contains a policy module that makes decisions on whether to reread, skim or stop, which is rewarded for both classification accuracy and computation cost. The entire architecture is trained end-to-end with backpropagation, Monte Carlo rollouts and a baseline for variance reduction. The results show that the architecture is able to classify accurately on all syntactic levels, faster than a baseline that reads the entire text. The approach is simple and seems to work well and could be applied to other tasks where inference time is important.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Fast and Accurate Text Classification: Skimming, Rereading and Early Stopping
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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The paper present a model for fast reading for text classification with mechanisms that allow the model to reread, skip words, or classify early before reading the entire review. The model contains a policy module that makes decisions on whether to reread, skim or stop, which is rewarded for both classification accuracy and computation cost. The entire architecture is trained end-to-end with backpropagation, Monte Carlo rollouts and a baseline for variance reduction. The results show that the architecture is able to classify accurately on all syntactic levels, faster than a baseline that reads the entire text. The approach is simple and seems to work well and could be applied to other tasks where inference time is important.
assistant
 The paper present a model for fast reading for text classification with mechanisms that allow the model to reread, skip words, or classify early before reading the entire review. The model contains a policy module that makes decisions on whether to reread, skim or stop, which is rewarded for both classification accuracy and computation cost. The entire architecture is trained end-to-end with backpropagation, Monte Carlo rollouts and a baseline for variance reduction. The results show that the architecture is able to classify accurately on all syntactic levels, faster than a baseline that reads the entire text. The approach is simple and seems to work well and could be applied to other tasks where inference time is important.