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.
@article{arxiv.2003.11184,
title = {Adversarial Multi-Binary Neural Network for Multi-class Classification},
author = {Haiyang Xu and Junwen Chen and Kun Han and Xiangang Li},
journal= {arXiv preprint arXiv:2003.11184},
year = {2020}
}