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So, how can we more efficiently detect sarcasm meanings from videos? Our work will make the following three contributions:
Firstly, We propose a novel utterance-level incongruity learning network for multimodal sarcasm detection. The core module is multimodal utterance-level attention (M-ULA) which relies on utterance-level to extract unimodal and multimodal features and effectively explore the complex interrelationships present in multimodal data.
Secondly, To capture the incongruity information, we design an incongruity learning network (ILN) which consists of primary modality choosing, cross attention mechanism and self-attention mechanism to explore sufficient incongruity information.
Lastly, we conducted extensive experiments and comparisons on a public datasets to demonstrate the superiority of the proposed method. Additionally, we conducted ablation experiments, which demonstrated the rationality and necessity of the proposed method.
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IEEE/ICACT20240342 Slide.03
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