  The task of text classification is usually divided into two stages: {\it text
feature extraction} and {\it classification}. In this standard formalization
categories are merely represented as indexes in the label vocabulary, and the
model lacks for explicit instructions on what to classify. Inspired by the
current trend of formalizing NLP problems as question answering tasks, we
propose a new framework for text classification, in which each category label
is associated with a category description. Descriptions are generated by
hand-crafted templates or using abstractive/extractive models from
reinforcement learning. The concatenation of the description and the text is
fed to the classifier to decide whether or not the current label should be
assigned to the text. The proposed strategy forces the model to attend to the
most salient texts with respect to the label, which can be regarded as a hard
version of attention, leading to better performances. We observe significant
performance boosts over strong baselines on a wide range of text classification
tasks including single-label classification, multi-label classification and
multi-aspect sentiment analysis.
