Sarcasm Detection

The goal of Sarcasm Detection is to determine whether a sentence is sarcastic or non-sarcastic. Sarcasm is a type of phenomenon with specific perlocutionary effects on the hearer, such as to break their pattern of expectation. Consequently, correct understanding of sarcasm often requires a deep understanding of multiple sources of information, including the utterance, the conversational context, and, frequently some real world facts.

Source: Attentional Multi-Reading Sarcasm Detection

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 26–50 of 266 papers

Title	Date	Tasks	Status	Hype	Score
News Headlines Dataset For Sarcasm Detection	Sep 17, 2022	ArticlesSarcasm Detection	CodeCode Available	1	5
Sarcasm Detection using Hybrid Neural Network	Aug 20, 2019	Sarcasm Detection	CodeCode Available	1	5
Multi-Modal Sarcasm Detection Based on Contrastive Attention Mechanism	Sep 30, 2021	Sarcasm Detection	CodeCode Available	1	5
Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal	Sep 1, 2017	Sarcasm Detection	CodeCode Available	0	5
A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict	Sep 8, 2021	Sarcasm DetectionSentence	CodeCode Available	0	5
"Laughing at you or with you": The Role of Sarcasm in Shaping the Disagreement Space	Jan 26, 2021	ClassificationGeneral Classification	CodeCode Available	0	5
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks	Oct 27, 2016	Sarcasm DetectionSentence	CodeCode Available	0	5
``Laughing at you or with you'': The Role of Sarcasm in Shaping the Disagreement Space	Apr 1, 2021	ClassificationRelation	CodeCode Available	0	5
KoCoSa: Korean Context-aware Sarcasm Detection Dataset	Feb 22, 2024	Dataset GenerationSarcasm Detection	CodeCode Available	0	5
Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification	Mar 27, 2023	ClassificationHate Speech Detection	CodeCode Available	0	5
IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detection	May 22, 2025	In-Context LearningSarcasm Detection	CodeCode Available	0	5
Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection	Apr 19, 2021	Meta-LearningSarcasm Detection	CodeCode Available	0	5
LT3 at SemEval-2022 Task 6: Fuzzy-Rough Nearest Neighbor Classification for Sarcasm Detection	Jul 1, 2022	Binary ClassificationClassification	CodeCode Available	0	5
How Effective is Incongruity? Implications for Code-mix Sarcasm Detection	Feb 6, 2022	Sarcasm Detection	CodeCode Available	0	5
A Large Self-Annotated Corpus for Sarcasm	Apr 19, 2017	Sarcasm Detection	CodeCode Available	0	5
Gender Bias Mitigation for Bangla Classification Tasks	Nov 16, 2024	ClassificationHate Speech Detection	CodeCode Available	0	5
How effective is incongruity? Implications for code-mixed sarcasm detection	Dec 1, 2021	Sarcasm Detection	CodeCode Available	0	5
Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues	Nov 20, 2022	Emotion RecognitionNatural Language Understanding	CodeCode Available	0	5
BloombergGPT: A Large Language Model for Finance	Mar 30, 2023	Causal JudgmentCommon Sense Reasoning	CodeCode Available	0	5
Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!	Apr 9, 2024	Sarcasm Detection	CodeCode Available	0	5
Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour	May 25, 2020	Multi-Task LearningNamed Entity Recognition (NER)	CodeCode Available	0	5
An Investigation into the Contribution of Locally Aggregated Descriptors to Figurative Language Identification	Nov 1, 2021	Language IdentificationNatural Language Understanding	CodeCode Available	0	5
AlexU-AL at SemEval-2022 Task 6: Detecting Sarcasm in Arabic Text Using Deep Learning Techniques	Jul 1, 2022	Natural Language UnderstandingSarcasm Detection	CodeCode Available	0	5
CASCADE: Contextual Sarcasm Detection in Online Discussion Forums	May 16, 2018	General ClassificationSarcasm Detection	CodeCode Available	0	5
FiLMing Multimodal Sarcasm Detection with Attention	Aug 9, 2021	Opinion MiningSarcasm Detection	CodeCode Available	0	5

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All datasets BIG-bench (SNARKS)FigLang 2020 Reddit Dataset FigLang 2020 Twitter Dataset SARC (all-bal)SARC (pol-bal)iSarcasm MUStARD SARC (pol-unbal)WITS

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	PaLM 2(few-shot, k=3, CoT)	Accuracy	84.8	—	Unverified
2	PaLM 2 (few-shot, k=3, Direct)	Accuracy	78.7	—	Unverified
3	PaLM 540B (few-shot, k=3)	Accuracy	78.1	—	Unverified
4	BLOOM 176B (few-shot, k=3)	Accuracy	72.47	—	Unverified
5	Bloomberg GPT (few-shot, k=3)	Accuracy	69.66	—	Unverified
6	GPT-NeoX (few-shot, k=3)	Accuracy	62.36	—	Unverified
7	Chinchilla-70B (few-shot, k=5)	Accuracy	58.6	—	Unverified
8	Gopher-280B (few-shot, k=5)	Accuracy	48.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BERT+Aspect-based approaches	F1	0.74	—	Unverified
2	RoBERTa_large - (Separated Context-Response)	F1	0.72	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBERTa_large (Context-Response)	F1	0.77	—	Unverified
2	BERT	F1	0.73	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CASCADE	Accuracy	77	—	Unverified
2	Bag-of-Bigrams	Accuracy	75.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Bag-of-Bigrams	Accuracy	76.5	—	Unverified
2	CASCADE	Accuracy	74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBERTa + Mutation Data Augmentation	F1-Score	0.41	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MUStARD++	Precision	70.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Bag-of-Words	Avg F1	27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BART	R1	36.88	—	Unverified