Unimodal Training for Multimodal Meme Sentiment Classification: Hyperparameters and Settings

Written by @memeology | Published on 2024/4/7

TL;DR —

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

Abstract and Introduction

Related Works

Methodology

Results

Limitations and Future Works

Conclusion, Acknowledgments, and References

A Hyperparameters and Settings

B Metric: Weighted F1-Score

C Architectural Details

D Performance Benchmarking

E Contingency Table: Baseline vs. Text-STILT

A Hyperparameters and Settings

Table 6: Hyperparameter values and settings used during model training by input type.

This paper is available on arxiv under CC 4.0 license.

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Written by

@memeology

Memes are cultural items transmitted by repetition in a manner analogous to the biological transmission of genes.

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