Explore a novel approach to improve multimodal meme sentiment classification by supplementing training with unimodal sentiment data.
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.
approach entails three steps: 1. Load pretrained weights into a classifier model. 2. Finetune the model on a supervised learning task for which data is easily available . 3. Finetune the model on a data-scarce task that is distinct to the intermediate task. is particularly effective in target tasks in NLU with smaller datasets, e.g. WiC and BoolQ . However, they also showed that the performance benefits of this approach are inconsistent and depend on choosing appropriate intermediate tasks for any given target task. In some cases, intermediate training was found to be detrimental to target task performance; which Pruksachatkun et al.
that uses unimodal sentiment analysis data as an intermediate task in training a multimodal meme sentiment classifier, we answer the following questions: : Does supplementing the training of a multimodal meme classifier with unimodal sentiment data significantly improve its performance? RQ1 We separately tested our proposed approach with image-only and text-only 3-class sentiment data as illustrated in Figure 1).
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