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