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Okay, in this paper, we investigate a comprehensive and robust approach that employs an Utterance-level Incongruity Learning Network (ULIL) to address multimodal sarcasm detection tasks. Unlike previous works, we extract utterance-level unimodal features and multimodal features fused by a multimodal utterance-level attention. Additionally, we explore an incongruity learning network for sarcasm detection. Specifically, considering the sarcasm utterances process incongruous characteristics, we deploy a cross-attention and self-attention mechanism to acquire incongruities between primary modality and auxiliary modalities. Our proposed model has been experimentally tested on a public benchmark dataset, demonstrating a significant performance improvement compared to a series of baselines for the multimodal sarcasm detection task.
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IEEE/ICACT20240342 Slide.18
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