| 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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| 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. |
Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network (2022.acl-long)
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| Challenge: | Existing studies on multimodal sarcasm detection using textual and visual information have been limited to text-only approaches. |
| Approach: | They propose to construct a cross-modal graph for each multi-modal instance to explicitly draw the ironic relations between textual and visual modalities. |
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Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic Association (2020.acl-main)
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| Challenge: | Existing methods for sarcasm detection rely on text data, but are insufficient to detect multimodal sarcasm. |
| Approach: | They propose a method for modeling cross-modality contrast in the associated context by constructing the Decomposition and Relation Network. |
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Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model (P19-1)
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| Challenge: | Existing methods to detect sarcasm focus on text, but they are insufficient for multi-modal messages. |
| Approach: | They propose a multi-modal hierarchical sarcasm detection model for tweets consisting of texts and images in Twitter. |
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Scale Is All You Need: Analyzing Modality Interaction and Speaker Intent Without Fine-Tuning (2026.eacl-srw)
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| Challenge: | Recent work on sarcasm and humor detection uses large multimodal Transformers, but they are computationally expensive and opaque. |
| Approach: | They propose a lightweight framework for multimodal sarcasm detection that combines frozen text, audio, and visual embeddings from pretrained encoders through compact fusion heads. |
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A Multimodal Corpus for Emotion Recognition in Sarcasm (2022.lrec-1)
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| Challenge: | sarcasm and emotion are often used in conversational systems to generate the right response. |
| Approach: | They use a sarcastic expression dataset pre-annotated with 9 emotions to detect emotion . they identify and correct 343 incorrect emotion labels and label each sarkastic utterance with one of four sarcasm types. |
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Multi-modal Sarcasm Generation: Dataset and Solution (2023.findings-acl)
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| Challenge: | Existing studies on sarcasm generation do not consider generating sarcasastic descriptions for a given image with hashtags that provide the sarkastic target. |
| Approach: | They propose a multi-modal Sarcasm generation task that generates sarcastic descriptions like humans using images, hashtags, and OCR tokens. |
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SarcNet: A Multilingual Multimodal Sarcasm Detection Dataset (2024.lrec-main)
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| Challenge: | Sarcasm is an implicit form of sarcasm, involving an intended meaning that contradicts the literal expression . human use conflict between factual information and a statement as cues to detect sarcasm . sarkasmatic analysis is challenging due to its implicit nature . |
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Leveraging Generative Large Language Models with Visual Instruction and Demonstration Retrieval for Multimodal Sarcasm Detection (2024.naacl-long)
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| Challenge: | Existing methods for multimodal sarcasm detection do not fully utilize cross-modal features, limiting their performance on in-domain datasets. |
| Approach: | They propose a multimodal sarcasm detection model with a designed instruction template and a demonstration retrieval module. |
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A deep-learning framework to detect sarcasm targets (D19-1)
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| Challenge: | Existing methods for sarcasm target detection are difficult to implement in natural language processing. |
| Approach: | They propose a deep learning framework for sarcasm target detection in predefined sarkastic texts. |
| Outcome: | The proposed framework improves accuracy and accuracy in match and dice scores compared to the current state-of-the-art framework. |