  Multi-class text classification is one of the key problems in machine
learning and natural language processing. Emerging neural networks deal with
the problem using a multi-output softmax layer and achieve substantial
progress, but they do not explicitly learn the correlation among classes. In
this paper, we use a multi-task framework to address multi-class
classification, where a multi-class classifier and multiple binary classifiers
are trained together. Moreover, we employ adversarial training to distinguish
the class-specific features and the class-agnostic features. The model benefits
from better feature representation. We conduct experiments on two large-scale
multi-class text classification tasks and demonstrate that the proposed
architecture outperforms baseline approaches.
