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.

中文翻译：

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用于多类分类的对抗性多二元神经网络

多类文本分类是机器学习和自然语言处理中的关键问题之一。新兴神经网络使用多输出 softmax 层处理该问题并取得了实质性进展，但它们并没有明确学习类之间的相关性。在本文中，我们使用多任务框架来解决多类分类问题，其中多类分类器和多个二元分类器一起训练。此外，我们采用对抗性训练来区分特定于类的特征和与类无关的特征。该模型受益于更好的特征表示。我们对两个大规模多类文本分类任务进行了实验，并证明所提出的架构优于基线方法。