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Multi-modal sarcasm detection with interactive in-modal and cross-modal graphs
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Liang, Bin, Lou, Chenwei, Li, Xiang, Gui, Lin, Yang, Min and Xu, Ruifeng (2021) Multi-modal sarcasm detection with interactive in-modal and cross-modal graphs. In: 29th ACM International Conference on Multimedia, Virtual ; Chengdu, China, 20-24 Oct 2021. Published in: MM '21: Proceedings of the 29th ACM International Conference on Multimedia pp. 4707-4715. ISBN 9781450386517. doi:10.1145/3474085.3475190
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Official URL: https://doi.org/10.1145/3474085.3475190
Abstract
Sarcasm is a peculiar form and sophisticated linguistic act to express the incongruity of someone's implied sentiment expression, which is a pervasive phenomenon in social media platforms. Compared with sarcasm detection purely on texts, multi-modal sarcasm detection is more adapted to the rapidly growing social media platforms, where people are interested in creating multi-modal messages. When focusing on the multi-modal sarcasm detection for tweets consisting of texts and images on Twitter, the significant clue of improving the performance of multi-modal sarcasm detection evolves into how to determine the incongruity relations between texts and images. In this paper, we investigate multi-modal sarcasm detection from a novel perspective, so as to determine the sentiment inconsistencies within a certain modality and across different modalities by constructing heterogeneous in-modal and cross-modal graphs (InCrossMGs) for each multi-modal example. Based on it, we explore an interactive graph convolution network (GCN) structure to jointly and interactively learn the incongruity relations of in-modal and cross-modal graphs for determining the significant clues in sarcasm detection. Experimental results demonstrate that our proposed model achieves state-of-the-art performance in multi-modal sarcasm detection.
Item Type: | Conference Item (Paper) | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||
SWORD Depositor: | Library Publications Router | ||||
Journal or Publication Title: | MM '21: Proceedings of the 29th ACM International Conference on Multimedia | ||||
Publisher: | ACM | ||||
ISBN: | 9781450386517 | ||||
Official Date: | 17 October 2021 | ||||
Dates: |
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Page Range: | pp. 4707-4715 | ||||
DOI: | 10.1145/3474085.3475190 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access | ||||
Conference Paper Type: | Paper | ||||
Title of Event: | 29th ACM International Conference on Multimedia | ||||
Type of Event: | Conference | ||||
Location of Event: | Virtual ; Chengdu, China | ||||
Date(s) of Event: | 20-24 Oct 2021 |
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