  Document classification tasks were primarily tackled at word level. Recent
research that works with character-level inputs shows several benefits over
word-level approaches such as natural incorporation of morphemes and better
handling of rare words. We propose a neural network architecture that utilizes
both convolution and recurrent layers to efficiently encode character inputs.
We validate the proposed model on eight large scale document classification
tasks and compare with character-level convolution-only models. It achieves
comparable performances with much less parameters.
