Robust Text Classifier on Test-Time Budgets
In this paper, we design a generic framework for learning a robust text classification model that achieves accuracy comparable to standard full models under test-time budget constraints. We take a different approach from existing methods and learn to dynamically delete a large fraction of unimportant words by a low-complexity selector such that the high-complexity classifier only needs to process a small fraction of important words. In addition, we propose a new data aggregation method to train the classifier, allowing it to make accurate predictions even on fragmented sequence of words. Our end-to-end method achieves state-of-the-art performance while its computational complexity scales linearly with the small fraction of important words in the whole corpus. Besides, a single deep neural network classifier trained by our framework can be dynamically tuned to different budget levels at inference time.
 This paper proposes a framework for fast text classification that relies on a text selector and classifier that are jointly optimized using an aggregation scheme for the output of the text selector. This has an interesting application in cases where the text selector has to be implemented in a low performance device and the classification can be done on the cloud. The approach the authors take is interesting and different from what has done before, however I believe some further details / tests are needed to make this paper more convincing. It doesn’t seem like the model has a clear advantage over existing methods. The advantage of the method could be brought up by comparing other complexity metrics such as how many words need to be transmitted (this is shown in figure 2 but not in relation to other approaches), what is a possible other advantage over the skim-RNNs etc. The interpretability of the results is encouraging and perhaps this could be explored more and leveraged as an advantage.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Robust Text Classifier on Test-Time Budgets
In this paper, we design a generic framework for learning a robust text classification model that achieves accuracy comparable to standard full models under test-time budget constraints. We take a different approach from existing methods and learn to dynamically delete a large fraction of unimportant words by a low-complexity selector such that the high-complexity classifier only needs to process a small fraction of important words. In addition, we propose a new data aggregation method to train the classifier, allowing it to make accurate predictions even on fragmented sequence of words. Our end-to-end method achieves state-of-the-art performance while its computational complexity scales linearly with the small fraction of important words in the whole corpus. Besides, a single deep neural network classifier trained by our framework can be dynamically tuned to different budget levels at inference time.
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>



 This paper proposes a framework for fast text classification that relies on a text selector and classifier that are jointly optimized using an aggregation scheme for the output of the text selector. This has an interesting application in cases where the text selector has to be implemented in a low performance device and the classification can be done on the cloud. The approach the authors take is interesting and different from what has done before, however I believe some further details / tests are needed to make this paper more convincing. It doesn’t seem like the model has a clear advantage over existing methods. The advantage of the method could be brought up by comparing other complexity metrics such as how many words need to be transmitted (this is shown in figure 2 but not in relation to other approaches), what is a possible other advantage over the skim-RNNs etc. The interpretability of the results is encouraging and perhaps this could be explored more and leveraged as an advantage.
assistant
 This paper proposes a framework for fast text classification that relies on a text selector and classifier that are jointly optimized using an aggregation scheme for the output of the text selector. This has an interesting application in cases where the text selector has to be implemented in a low performance device and the classification can be done on the cloud. The approach the authors take is interesting and different from what has done before, however I believe some further details / tests are needed to make this paper more convincing. It doesn’t seem like the model has a clear advantage over existing methods. The advantage of the method could be brought up by comparing other complexity metrics such as how many words need to be transmitted (this is shown in figure 2 but not in relation to other approaches), what is a possible other advantage over the skim-RNNs etc. The interpretability of the results is encouraging and perhaps this could be explored more and leveraged as an advantage.