Adversarial Multi-Binary Neural Network for Multi-class Classification

Haiyang Xu; Junwen Chen; Kun Han; Xiangang Li

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arXiv · 2003.11184

Adversarial Multi-Binary Neural Network for Multi-class Classification

Haiyang Xu·Junwen Chen·Kun Han·Xiangang Li

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Abstract

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.

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BibTeX RIS

Haiyang Xu, Junwen Chen, Kun Han, Xiangang Li. 2020-03-25. Adversarial Multi-Binary Neural Network for Multi-class Classification. https://arxiv.org/abs/2003.11184

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