Zero-shot stance detection based on multi-perspective transferable feature fusion

Zhao, Xuechen; Zou, Jiaying; Miao, Jinfeng; Tian, Lei; Gao, Liqun; Zhou, Bin; Pang, Shengnan

doi:10.1016/j.inffus.2024.102386

Information Fusion

2024

DOI: 10.1016/j.inffus.2024.102386

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Zero-shot stance detection based on multi-perspective transferable feature fusion

Xuechen Zhao,

Jiaying Zou,

Jinfeng Miao

et al.

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2024

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Zero-shot stance detection based on multi-expert collaboration

Zhao,

Ma,

Pang

et al. 2024

Sci Rep

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Zero-shot stance detection is pivotal for autonomously discerning user stances on novel emerging topics. This task hinges on effective feature alignment transfer from known to unseen targets. To address this, we introduce a zero-shot stance detection framework utilizing multi-expert cooperative learning. This framework comprises two core components: a multi-expert feature extraction module and a gating mechanism for stance feature selection. Our approach involves a unique learning strategy tailored to decompose complex semantic features. This strategy harnesses the expertise of multiple specialists to unravel and learn diverse, intrinsic textual features, enhancing transferability. Furthermore, we employ a gating-based mechanism to selectively filter and fuse these intricate features, optimizing them for stance classification. Extensive experiments on standard benchmark datasets demonstrate that our model significantly surpasses existing baseline models in performance.

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Zero-shot stance detection based on multi-expert collaboration

Zhao,

Ma,

Pang

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

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Zero-shot stance detection based on multi-perspective transferable feature fusion

Cited by 1 publication

References 33 publications

Zero-shot stance detection based on multi-expert collaboration

Zero-shot stance detection based on multi-expert collaboration

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