  We propose MVCNN, a convolution neural network (CNN) architecture for
sentence classification. It (i) combines diverse versions of pretrained word
embeddings and (ii) extracts features of multigranular phrases with
variable-size convolution filters. We also show that pretraining MVCNN is
critical for good performance. MVCNN achieves state-of-the-art performance on
four tasks: on small-scale binary, small-scale multi-class and largescale
Twitter sentiment prediction and on subjectivity classification.
