Discover the limitations and future prospects of Text-STILT in meme sentiment analysis, exploring challenges, opportunities, and the future of meme classifiers.
Authors: Muzhaffar Hazman, University of Galway, Ireland; Susan McKeever, Technological University Dublin, Ireland; 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.
In the future, we plan to reformulate Image-STILT with respect to the approach and data used to isolate the cause of its non-performance on the downstream task. Furthermore, we did not test Text-STILT on classifiers that represent the image modality of a meme in textual forms, as others did . Notwithstanding our results, Text-STILT may not benefit all multimodal meme classifiers. Phang et al. showed that STILT offers varying degrees of benefit depending on the encoders chosen.
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