标题： 多领域文本分类的双重对抗协同学习 显示英文标题

标题： Dual Adversarial Co-Learning for Multi-Domain Text Classification

摘要： 本文提出了一种新颖的多域文本分类（MDTC）双重对抗性协同学习方法。该方法学习共享和私有网络以提取特征，并部署双重对抗性正则化，在基于差异的协同学习框架下同时对齐不同领域以及标记数据和未标记数据之间的特征，旨在通过所学特征提高分类器的泛化能力。我们在多域情感分类数据集上进行了实验。结果显示，所提出的模型达到了最先进的MDTC性能。 显示英文摘要

摘要： 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.