| Challenge: | Sarcasm is a figurative speech act which manifests on social networks such as Twitter and Reddit. |
| Approach: | They propose a model that looks in-between rather than across to explicitly model contrast and incongruity. |
| Outcome: | The proposed model achieves state-of-the-art performance on all datasets and improves interpretability. |
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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 . |
“Laughing at you or with you”: The Role of Sarcasm in Shaping the Disagreement Space (2021.eacl-main)
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| Challenge: | Detecting arguments in online interactions is useful to understand how conflicts arise and get resolved. |
| Approach: | They propose to use a corpus annotated with argumentative moves and sarcasm to model sarcastic relationships using deep learning architectures. |
| Outcome: | The proposed setup improves the argumentative relation classification task using deep learning architectures. |
Sarcasm-R1: Enhancing Sarcasm Detection through Focused Reasoning (2025.findings-emnlp)
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| Challenge: | Existing methods for sarcasm detection are limited by supervised learning or prompt engineering . a new approach decomposes sarcasm detection into three dimensions: language, context, and emotion . |
| Approach: | They propose a method that decomposes sarcasm detection into three dimensions: language, context, and emotion. |
| Outcome: | The proposed method outperforms state-of-the-art methods in most cases. |
Perceived and Intended Sarcasm Detection with Graph Attention Networks (2021.findings-emnlp)
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| Challenge: | Existing sarcasm detection systems focus on exploiting linguistic markers, context, or user-level priors, but social studies suggest that the relationship between the author and the audience can be equally relevant for the sarkasmal usage and interpretation. |
| Approach: | They propose a framework leveraging a user context from their historical tweets together with social information from a users neighborhood in an interaction graph to contextualize the interpretation of the post. |
| Outcome: | The proposed framework combines a user context from their historical tweets with social information from a users neighborhood in an interaction graph to contextualize the interpretation of the post. |
Exploring Author Context for Detecting Intended vs Perceived Sarcasm (P19-1)
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| Challenge: | Existing studies on textual sarcasm detection use manual labelling and tag-based distant supervision to detect sarcasm. |
| Approach: | They define author context as the embedded representation of their historical tweets and suggest neural models that extract these representations. |
| Outcome: | The proposed models achieve state-of-the-art on two datasets labelled manually and via tag-based distant supervision indicating a difference between intended and perceived sarcasm . |
iSarcasm: A Dataset of Intended Sarcasm (2020.acl-main)
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| Challenge: | Existing methods for detecting intended sarcasm have shown low performance compared to previous studies. |
| Approach: | They propose a dataset of tweets labeled for intended sarcasm by their authors . they aim to encourage future NLP research to develop methods for detecting sarkasmus in text as intended by the authors of the text . |
| Outcome: | The proposed model shows that existing methods are biased or obvious and sarcasm could be understudied. |
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning (2025.coling-main)
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| Challenge: | Existing methods for sarcasm detection lack commonsense inferential ability when faced with complex situations. |
| Approach: | They propose a commonsense reasoning framework for sarcasm detection based on commonsensense augmentation to supplement commonsence knowledge and infer the incongruity. |
| Outcome: | The proposed framework is able to detect sarcasm in five datasets and is robust to complex scenarios. |
Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement (2022.emnlp-main)
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| Challenge: | Sarcasm is a linguistic phenomenon indicating a discrepancy between literal meanings and implied intentions. |
| Approach: | They propose a hierarchical framework for sarcasm detection by exploring atomic-level congruity and composition-level convergence. |
| Outcome: | The proposed model outperforms existing methods on a public sarcasm detection dataset based on Twitter . |
Representing Social Media Users for Sarcasm Detection (D18-1)
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| Challenge: | Existing annotated corpus of Reddit comments is limited by available annotation methods. |
| Approach: | They propose a Bayesian approach that directly represents authors’ propensities to be sarcastic and a dense embedding approach that can learn interactions between the author and the text. |
| Outcome: | The proposed approach performs better in homogeneous contexts, whereas the dense embeddings prove valuable in more diverse contexts. |
Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction (2023.acl-long)
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| Challenge: | sarcasm is a form of irony conveying mockery and contempt . social media has become increasingly popular for identifying sarcasm . |
| Approach: | They develop a method to detect sarcasm from social media using augmented potentials. |
| Outcome: | The proposed method outperforms baselines on benchmark datasets. |