Unseen Target Stance Detection with Adversarial Domain Generalization

被引：4

作者：

Wang, Zhen ^{[1
]}

Wang, Qiansheng ^{[1
]}

Lv, Chengguo ^{[1
]}

Cao, Xue ^{[1
]}

Fu, Guohong ^{[2
]}

机构：

[1] Heilongjiang Univ, Sch Comp Sci & Technol, Harbin, Peoples R China

[2] Soochow Univ, Inst Artificial Intelligence, Suzhou, Peoples R China

来源：

2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2020年

基金：

中国国家自然科学基金;

关键词：

stance detection; adversarial domain generalization; transfer learning; attention;

D O I：

10.1109/ijcnn48605.2020.9206635

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Although stance detection has made great progress in the past few years, it is still facing the problem of unseen targets. In this study, we investigate the domain difference between targets and thus incorporate attention-based conditional encoding with adversarial domain generalization to perform unseen target stance detection. Experimental results show that our approach achieves new state-of-the-art performance on the SemEval-2016 dataset, demonstrating the importance of domain difference between targets in unseen target stance detection.

引用

页数：8

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