Paper Info
Title
Multi-Modal Sarcasm Detection with Interactive In-Modal and Cross-Modal Graphs
Abstract
ABSTRACTSarcasm 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.
Year
DOI
Venue
2021
10.1145/3474085.3475190
International Multimedia Conference
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Bin Liang153.81
Chenwei Lou221.30
Xiang Li38140.11
Lin Gui4186.43
Min Yang57720.41
Xu Ruifeng643253.04