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Multi-modal sarcasm detection via cross-modal graph convolutional network
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Liang, Bin, Lou, Chenwei, Li, Xiang, Yang, Min, Gui, Lin, He, Yulan, Pei, Wenjie and Xu, Ruifeng (2022) Multi-modal sarcasm detection via cross-modal graph convolutional network. 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. 1767-1777. doi:10.18653/v1/2022.acl-long.124
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Official URL: http://dx.doi.org/10.18653/v1/2022.acl-long.124
Abstract
With the increasing popularity of posting multimodal messages online, many recent studies have been carried out utilizing both textual and visual information for multi-modal sarcasm detection. In this paper, we investigate multimodal sarcasm detection from a novel perspective by constructing a cross-modal graph for each instance to explicitly draw the ironic relations between textual and visual modalities. Specifically, we first detect the objects paired with descriptions of the image modality, enabling the learning of important visual information. Then, the descriptions of the objects are served as a bridge to determine the importance of the association between the objects of image modality and the contextual words of text modality, so as to build a cross-modal graph for each multi-modal instance. Furthermore, we devise a cross-modal graph convolutional network to make sense of the incongruity relations between modalities for multi-modal sarcasm detection. Extensive experimental results and in-depth analysis show that our model achieves state-of-the-art performance in multi-modal sarcasm detection.
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 T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||||||||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Computer vision , Neural networks (Computer science), Graph theory, Natural language processing (Computer science), Data mining -- Online social networks, Machine learning, Computational intelligence, Text processing (Computer science), Irony , 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. 1767-1777 | ||||||||||||||||||||||||||||||||||||
DOI: | 10.18653/v1/2022.acl-long.124 | ||||||||||||||||||||||||||||||||||||
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: | 21 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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