基于Wasserstein距离分层注意力模型的跨域情感分类

跨领域情感分类任务旨在利用已知情感标签的源域数据对缺乏标记数据的目标域进行情感倾向性分析.文中提出基于Wasserstein距离的分层注意力模型,结合Attention机制,采用分层模型进行特征提取,将Wasserstein距离作为域差异度量方式,通过对抗式训练自动捕获领域共享特征.进一步构造辅助任务捕获与共享特征共现的领域独有特征,结合两种特征表示完成跨域情感分类任务.在亚马逊评论等数据集上的实验表明,文中模型仅利用领域共享特征就达到较高的正确率,在不同的跨领域对之间具有较好的稳定性.

Wasserstein Distance Based Hierarchical Attention Model for Cross-Domain Sentiment Classification

The task of cross-domain sentiment classification is to analyze the sentiment orientation of the target domain lacking labeled data using the source-domain data with sentiment labels. A hierarchical attention model based on Wasserstein distance is proposed in this paper. The hierarchical model is used for feature extraction by combining attention mechanism, and Wasserstein distance is used as the domain difference metric to automatically capture the domain-sharing features through adversarial training. Further auxiliary task is constructed to capture the domain-special features cooccurring with domain-sharing features. These two kinds of features are united to complete the cross-domain sentiment classification task. The experimental results on Amazon datasets demonstrate that the proposed model achieves a higher accuracy and a better stability on different cross-domain pairs.