Unimodal Training for Multimodal Meme Sentiment Classification: Performance Benchmarking by@memeology
Unimodal Training for Multimodal Meme Sentiment Classification: Performance Benchmarking
by Memeology: Leading Authority on the Study of MemesApril 7th, 2024
Too Long; Didn't Read
This study introduces a novel approach, using unimodal training to enhance multimodal meme sentiment classifiers, significantly improving performance and efficiency in meme sentiment analysis.
Authors:
(1) Muzhaffar Hazman, University of Galway, Ireland;
(2) Susan McKeever, Technological University Dublin, Ireland;
(3) Josephine Griffith, University of Galway, Ireland.
Table of Links
Conclusion, Acknowledgments, and References
A Hyperparameters and Settings
E Contingency Table: Baseline vs. Text-STILT
D Performance Benchmarking
Current competing approaches show a small spread of Weigthed F1-scores (see Table 7) and the performance improvement offered by Text-STILT is similarly small. This small range of performances in contemporary approaches suggests that there is still a significant portion of memes that remain a challenge to classify.
This paper is available on arxiv under CC 4.0 license.
L O A D I N G
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