  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.
