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JointCL : a joint contrastive learning framework for zero-shot stance detection
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Liang, Bin, Zhu, Qinlin, Li, Xiang, Yang, Min, Gui, Lin, He, Yulan and Xu, Ruifeng (2022) JointCL : a joint contrastive learning framework for zero-shot stance detection. In: The 60th Annual Meeting of the Association for Computational Linguistics (ACL), Dublin, Ireland, 22-27 May 2022. Published in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1 pp. 81-91. doi:10.18653/v1/2022.acl-long.7
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WRAP-JointCL-joint-contrastive-learning-framework-zero-shot-stance-detection-2022.pdf - Accepted Version - Requires a PDF viewer. Download (3215Kb) | Preview |
Official URL: https://doi.org/10.18653/v1/2022.acl-long.7
Abstract
Zero-shot stance detection (ZSSD) aims to detect the stance for an unseen target during the inference stage. In this paper, we propose a joint contrastive learning (JointCL) framework, which consists of stance contrastive learning and target-aware prototypical graph contrastive learning. Specifically, a stance contrastive learning strategy is employed to better generalize stance features for unseen targets. Further, we build a prototypical graph for each instance to learn the target-based representation, in which the prototypes are deployed as a bridge to share the graph structures between the known targets and the unseen ones. Then a novel target-aware prototypical graph contrastive learning strategy is devised to generalize the reasoning ability of target-based stance representations to the unseen targets. Extensive experiments on three benchmark datasets show that the proposed approach achieves state-ofthe- art performance in the ZSSD task.
Item Type: | Conference Item (Paper) | ||||||||||||||||||||||||||||||||||||
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Subjects: | P Language and Literature > P Philology. Linguistics Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||||||||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Natural language processing (Computer science) , Sentiment analysis, Machine translating, Neural networks (Computer science), Machine learning, Computational linguistics | ||||||||||||||||||||||||||||||||||||
Journal or Publication Title: | Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | ||||||||||||||||||||||||||||||||||||
Publisher: | Association for Computational Linguistics | ||||||||||||||||||||||||||||||||||||
Official Date: | May 2022 | ||||||||||||||||||||||||||||||||||||
Dates: |
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Volume: | 1 | ||||||||||||||||||||||||||||||||||||
Page Range: | pp. 81-91 | ||||||||||||||||||||||||||||||||||||
DOI: | 10.18653/v1/2022.acl-long.7 | ||||||||||||||||||||||||||||||||||||
Status: | Peer Reviewed | ||||||||||||||||||||||||||||||||||||
Publication Status: | Published | ||||||||||||||||||||||||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||||||||||||||||||||||||||||
Date of first compliant deposit: | 17 March 2022 | ||||||||||||||||||||||||||||||||||||
Date of first compliant Open Access: | 18 March 2022 | ||||||||||||||||||||||||||||||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | ||||||||||||||||||||||||||||||||||||
Title of Event: | The 60th Annual Meeting of the Association for Computational Linguistics (ACL) | ||||||||||||||||||||||||||||||||||||
Type of Event: | Conference | ||||||||||||||||||||||||||||||||||||
Location of Event: | Dublin, Ireland | ||||||||||||||||||||||||||||||||||||
Date(s) of Event: | 22-27 May 2022 | ||||||||||||||||||||||||||||||||||||
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