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Question classification is an important task with wide applications. However,traditional techniques treat questions as general sentences, ignoring thecorresponding answer data. In order to consider answer information intoquestion modeling, we first introduce novel group sparse autoencoders whichrefine question representation by utilizing group information in the answerset. We then propose novel group sparse CNNs which naturally learn questionrepresentation with respect to their answers by implanting group sparseautoencoders into traditional CNNs. The proposed model significantly outperformstrong baselines on four datasets.

Group Sparse CNNs for Question Classification with Answer Sets