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.
Modular framework for document classification and data aggregation technique for making the framework robust to various distortion, and noise and focus only on the important words.  The authors consider training a RNN-based text classification where there is a resource restriction on test-time prediction, and provide an approach using a masking mechanism to reduce words/phrases/sentences used in prediction followed by a classifier to handle those components.