Dual Adversarial Co-Learning for Multi-Domain Text Classification

Yuan Wu; Yuhong Guo

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arXiv · 1909.08203

Dual Adversarial Co-Learning for Multi-Domain Text Classification

Yuan Wu·Yuhong Guo

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Abstract

In this paper we propose a novel dual adversarial co-learning approach for multi-domain text classification (MDTC). The approach learns shared-private networks for feature extraction and deploys dual adversarial regularizations to align features across different domains and between labeled and unlabeled data simultaneously under a discrepancy based co-learning framework, aiming to improve the classifiers' generalization capacity with the learned features. We conduct experiments on multi-domain sentiment classification datasets. The results show the proposed approach achieves the state-of-the-art MDTC performance.

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Yuan Wu, Yuhong Guo. 2019-09-18. Dual Adversarial Co-Learning for Multi-Domain Text Classification. https://arxiv.org/abs/1909.08203

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