  We introduce a novel, simple convolution neural network (CNN) architecture -
multi-group norm constraint CNN (MGNC-CNN) that capitalizes on multiple sets of
word embeddings for sentence classification. MGNC-CNN extracts features from
input embedding sets independently and then joins these at the penultimate
layer in the network to form a final feature vector. We then adopt a group
regularization strategy that differentially penalizes weights associated with
the subcomponents generated from the respective embedding sets. This model is
much simpler than comparable alternative architectures and requires
substantially less training time. Furthermore, it is flexible in that it does
not require input word embeddings to be of the same dimensionality. We show
that MGNC-CNN consistently outperforms baseline models.
