| 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 . |
| Approach: | They propose a multimodal sarcasm detection dataset that uses multiple modalities to detect sarcasm. |
| Outcome: | The proposed model improves on previous models based on a single label . human sarcasm cannot be detected using a unified label across multiple modalities . |
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Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, Roger Zimmermann, Rada Mihalcea, Soujanya Poria
| Challenge: | sarcasm is often expressed through multiple verbal and non-verbal cues, such as a change of tone, overemphasis, drawn-out syllables, or a straight looking face. |
| Approach: | They propose to use multimodal cues to improve sarcasm detection using audiovisual utterances annotated with sarcasm labels to improve the accuracy. |
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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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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. |
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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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A Large Self-Annotated Corpus for Sarcasm (L18-1)
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| Challenge: | Existing datasets for sarcasm detection have unbalanced and self-annotated labels, allowing for learning in both balanced and unbalanciated label regimes. |
| Approach: | They introduce the Self-Annotated Reddit Corpus (SARC) which has 1.3 million sarcastic statements and many times more instances of non-sarcasm statements. |
| Outcome: | The proposed corpus has 1.3 million sarcastic statements and many more instances of non-sarcasm statements, allowing for learning in both balanced and unbalanced label regimes. |
The Design and Construction of a Chinese Sarcasm Dataset (2020.lrec-1)
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| Challenge: | Existing sarcasm datasets are limited to English and Chinese . sarcasm is a multi-layered semi-conscious language phenomenon . |
| Approach: | They propose to build a high-quality Chinese sarcasm dataset using user comments . they use manual annotated sarkastic texts and non-sarcastic texts to train sarcasm classifier . |
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Multi-View Incongruity Learning for Multimodal Sarcasm Detection (2025.coling-main)
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Diandian Guo, Cong Cao, Fangfang Yuan, Yanbing Liu, Guangjie Zeng, Xiaoyan Yu, Hao Peng, Philip S. Yu
| Challenge: | Existing methods for multimodal sarcasm detection rely on spurious correlations, demonstrating poor generalizability beyond training environments. |
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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. |
| Outcome: | The proposed model can detect sarcasm in multimodal tweets using a dataset . |
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. |
| Outcome: | The proposed framework improves on the best unimodal baseline by combining text, audio, and visual embeddings from pretrained encoders with compact fusion heads. |
Multi-source Semantic Graph-based Multimodal Sarcasm Explanation Generation (2023.acl-long)
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| Challenge: | Existing studies can only identify sarcastic post but could not give concrete explanation for why it is sarkastic. |
| Approach: | They propose a multimodal sarcasm explanation scheme that generates a sentence for a social post to explain why it contains sarkasis. |
| Outcome: | The proposed model outperforms existing methods on a public dataset. |