research-article

Stance Detection with a Multi-Target Adversarial Attention Network

Authors:

Zhongqing Wang,

Huiyan XuAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 22, Issue 2

Article No.: 40, Pages 1 - 21

https://doi.org/10.1145/3544490

Published: 27 December 2022 Publication History

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.

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Cited By

Alturayeif NLuqman HAhmed M(2023)Enhancing stance detection through sequential weighted multi-task learningSocial Network Analysis and Mining10.1007/s13278-023-01169-714:1Online publication date: 9-Dec-2023
https://doi.org/10.1007/s13278-023-01169-7
Alturayeif NLuqman HAhmed M(2023)A systematic review of machine learning techniques for stance detection and its applicationsNeural Computing and Applications10.1007/s00521-023-08285-735:7(5113-5144)Online publication date: 28-Jan-2023
https://dl.acm.org/doi/10.1007/s00521-023-08285-7

Index Terms

Stance Detection with a Multi-Target Adversarial Attention Network
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 22, Issue 2

February 2023

624 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3572719

Editor:
Imed Zitouni
Google, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 27 December 2022

Online AM: 16 June 2022

Accepted: 06 June 2022

Revised: 15 May 2022

Received: 10 September 2021

Published in TALLIP Volume 22, Issue 2

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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

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Cited By

Alturayeif NLuqman HAhmed M(2023)Enhancing stance detection through sequential weighted multi-task learningSocial Network Analysis and Mining10.1007/s13278-023-01169-714:1Online publication date: 9-Dec-2023
https://doi.org/10.1007/s13278-023-01169-7
Alturayeif NLuqman HAhmed M(2023)A systematic review of machine learning techniques for stance detection and its applicationsNeural Computing and Applications10.1007/s00521-023-08285-735:7(5113-5144)Online publication date: 28-Jan-2023
https://dl.acm.org/doi/10.1007/s00521-023-08285-7

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