Stance Detection with a Multi-Target Adversarial Attention Network

Author:

Sun Qingying1ORCID,Xi Xuefeng2ORCID,Sun Jiajun1ORCID,Wang Zhongqing3ORCID,Xu Huiyan1ORCID

Affiliation:

1. School of Computer Science and Technology, Huaiyin Normal University, Huai’an, Jiangsu, China

2. School of Electronic & Information Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu, China

3. Natural Language Processing Lab, Soochow University, Suzhou, Jiangsu, China

Abstract

Stance detection aims to assign a stance label (in favor or against) to a post towards a specific target. In the literature, there are many studies focusing on this topic, and most of them treat stance detection as a supervised learning task. Therefore, a new classifier needs to be built from scratch on a well-prepared set of ground-truth data whenever predictions are needed for an unseen target. However, it is difficult to annotate the stance of a post, since a stance is a subjective attitude towards a target. Hence, it is necessary to learn the information from unlabeled data or other target data to help stance detection with a certain target. In this study, we propose a multi-target stance detection framework to integrate multi-target data together for stance detection. Since topic and sentiment are two important factors to identify the stance of a post in multi-target data, we propose an adversarial attention network to integrate multi-target data by detecting and connecting topic and sentiment information. In particular, the adversarial network is utilized to determine the topic and the sentiment of each post to collect some target-invariant information for stance detection. In addition, the attention mechanism is utilized to connect posts with a similar topic or sentiment to acquire some key information for stance detection. The experimental results not only demonstrate the effectiveness of the proposed model, but also indicate the importance of the topic and the sentiment information for stance detection using multi-target data.

Funder

National Natural Science Foundation of China

Qing Lan Project of Jiangsu Universities, Opening Foundation of Jiangsu Big Data Intelligent Engineering Laboratory of Soochow University

Project of Natural Science Research of Huai.an

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Reference54 articles.

1. Pranav Anand, Marilyn A. Walker, Rob Abbott, Jean E. Fox Tree, Robeson Bowmani, and Michael Minor. 2011. Cats rule and dogs drool!: Classifying stance in online debate. In Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis. Association for Computational Linguistics, 1–9.

2. Stance Detection with Bidirectional Conditional Encoding

3. SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling

4. Sihao Chen, Daniel Khashabi, Wenpeng Yin, Chris Callison-Burch, and Dan Roth. 2019. Seeing things from a different angle: Discovering diverse perspectives about claims. In Proceedings of NAACL-HLT 2019. 542–557.

5. AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Comparative learning based stance agreement detection framework for multi-target stance detection;Engineering Applications of Artificial Intelligence;2024-07

2. Enhancing stance detection through sequential weighted multi-task learning;Social Network Analysis and Mining;2023-12-09

3. Exploring the impact of training datasets on Turkish stance detection;Turkish Journal of Electrical Engineering and Computer Sciences;2023-11-30

4. A systematic review of machine learning techniques for stance detection and its applications;Neural Computing and Applications;2023-01-28

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3