The study introduces a new dataset for multimodal hyperbole detection, emphasizing the role of images in expressing hyperbole. Various fusion methods are evaluated, showing the significance of deep fusion for accurate detection. Pre-trained models like CLIP and BriVL perform poorly on this task. Cross-domain experiments highlight the challenges of generalization across different keywords.
The analysis reveals that images play a crucial role in detecting hyperbole, with deep fusion methods outperforming shallow ones. However, common sense knowledge is essential for accurate detection. The study also addresses ethical considerations regarding potentially controversial content in the dataset.
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by Huixuan Zhan... um arxiv.org 03-12-2024
https://arxiv.org/pdf/2307.00209.pdfTiefere Fragen