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The dominant approach for many NLP tasks are recurrent neural networks, inparticular LSTMs, and convolutional neural networks. However, thesearchitectures are rather shallow in comparison to the deep convolutionalnetworks which have pushed the state-of-the-art in computer vision. We presenta new architecture (VDCNN) for text processing which operates directly at thecharacter level and uses only small convolutions and pooling operations. We areable to show that the performance of this model increases with depth: using upto 29 convolutional layers, we report improvements over the state-of-the-art onseveral public text classification tasks. To the best of our knowledge, this isthe first time that very deep convolutional nets have been applied to textprocessing.

Very Deep Convolutional Networks for Text Classification