| Challenge: | Existing studies on sarcasm detection focus on lexical, syntactic and semantic cues, but sarcasm can be expressed implicitly without such cue. |
| Approach: | They propose a ContextuAl SarCasm DEtector which extracts contextual information from the discourse of a discussion thread. |
| Outcome: | The proposed model improves on a large Reddit corpus. |
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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. |
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. |
| Outcome: | The proposed model is able to detect sarcasm on twitter using three modalities . the proposed model can be used in customer service, opinion mining and harassment detection . |
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. |
RAM-SD: Retrieval-Augmented Multi-agent framework for Sarcasm Detection (2026.acl-long)
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| Challenge: | Existing approaches to sarcastic detection use a uniform reasoning strategy . existing approaches lack a framework to deal with the diverse analytical demands of sarcasm . |
| Approach: | They propose a Retrieval-Augmented Multi-Agent framework for Sarcasm Detection . the framework provides transparent and interpretable reasoning traces . |
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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 . |
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 . |
When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues (2022.acl-long)
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| Challenge: | Indirect speech achieves a constellation of discourse goals in human communication, but it is challenging for AI agents to comprehend such idiosyncrasies. |
| Approach: | They propose a task to generate natural language explanations of satirical conversations using a multimodal and code-mixed dataset to capture multimodality. |
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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. |
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. |
Affective and Contextual Embedding for Sarcasm Detection (2020.coling-main)
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| Challenge: | Existing methods to detect sarcasm from text lack vocal intonation or facial gestures in textual data. |
| Approach: | They propose two deep neural network models for sarcasm detection that extend the architecture of BERT by incorporating both affective and contextual features. |
| Outcome: | The proposed models outperform state-of-the-art models on different datasets with significant margins. |