  We propose a new deep neural network model and its training scheme for text
classification. Our model Sequence-to-convolution Neural Networks(Seq2CNN)
consists of two blocks: Sequential Block that summarizes input texts and
Convolution Block that receives summary of input and classifies it to a label.
Seq2CNN is trained end-to-end to classify various-length texts without
preprocessing inputs into fixed length. We also present Gradual Weight
Shift(GWS) method that stabilizes training. GWS is applied to our model's loss
function. We compared our model with word-based TextCNN trained with different
data preprocessing methods. We obtained significant improvement in
classification accuracy over word-based TextCNN without any ensemble or data
augmentation.
