Papers with Multimodal Sarcasm Target Identification

2 papers
CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models (2024.acl-long)

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Challenge: Current methods for multimodal sarcasm target identification focus on superficial indicators in an end-to-end manner, overlooking the nuanced understanding of multimodal content.
Approach: They propose a multimodal sarcasm target identification framework with a coarse-to-fine paradigm by augmenting sarcasm explainability with reasoning and pre-training knowledge.
Outcome: The proposed framework outperforms state-of-the-art methods and exhibits explainability in deciphering sarcasm as well.
Multimodal Sarcasm Target Identification in Tweets (2022.acl-long)

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Challenge: Existing methods to detect sarcasm target with text lacking context are not sufficient and complete.
Approach: They propose a multi-modal sarcasm target identification task that performs both textual and visual detection.
Outcome: The proposed model can perform textual target labeling and visual target detection.

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