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Dual-channel multi-level contrastive learning stance detection based on hybrid AutoEncoder

Cao Junhao
Zhu Xiaofei
Cheng Jiawei
School of Computer Science & Engineering, Chongqing University of Technology, Chongqing 400054, China

Abstract

Stance Detection aims to identify the author's attitudes or positions (agree, disagree, neutral) towards a specific text. Existing methods based solely on textual information exhibit limitations, particularly in cross-domain scenarios. To address this, research was conducted on leveraging social relationship information to enhance stance detection. Social relationship information was extracted from comments and replies and constructed into an inductive social relationship graph. A hybrid autoencoder stance detection framework, combining Autoencoder (AE) and Variational Autoencoder(VAE) , was proposed to precisely model the social relationship features of authors and improve stance recognition performance. To further learn robust social relationship features, a dual-channel multi-level contrastive learning method was designed. This method enhances the model's ability to capture social relationship by contrasting features within the hybrid autoencoder and across different social relationship graphs. Experiments on the DEBAGREEMENT dataset for in-domain and cross-domain tasks demonstrated that the proposed framework outperformed multiple baseline models. The findings validate the significance of social relationship information in stance detection tasks and provide new approaches and insights for future research.

Foundation Support

国家自然科学基金资助项目(62472059)
重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX1672)
重庆英才计划项目(CSTC2024YCJH-BGZXM0022)
重庆市教育委员会科学技术研究计划重大项目(KJZD-M202201102)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.10.0424
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 5

Publish History

[2025-03-06] Accepted Paper

Cite This Article

曹均皓, 朱小飞, 程佳玮. 基于混合自编码器的双通道多层次对比学习立场检测 [J]. 计算机应用研究, 2025, 42 (5). (2025-03-06). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0424. (Cao Junhao, Zhu Xiaofei, Cheng Jiawei. Dual-channel multi-level contrastive learning stance detection based on hybrid AutoEncoder [J]. Application Research of Computers, 2025, 42 (5). (2025-03-06). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0424. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.


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