  We propose a generic and interpretable learning framework for building robust
text classification model that achieves accuracy comparable to full models
under test-time budget constraints. Our approach learns a selector to identify
words that are relevant to the prediction tasks and passes them to the
classifier for processing. The selector is trained jointly with the classifier
and directly learns to incorporate with the classifier. We further propose a
data aggregation scheme to improve the robustness of the classifier. Our
learning framework is general and can be incorporated with any type of text
classification model. On real-world data, we show that the proposed approach
improves the performance of a given classifier and speeds up the model with a
mere loss in accuracy performance.
