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 151–160 of 266 papers

Title	Date	Tasks	Status
Human Centered NLP with User-Factor Adaptation	Sep 1, 2017	Document ClassificationDomain Adaptation	—Unverified
I2C at SemEval-2022 Task 6: Intended Sarcasm in English using Deep Learning Techniques	Jul 1, 2022	Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATION	—Unverified
I2C at SemEval-2022 Task 6: Intended Sarcasm Detection on Social Networks with Deep Learning	Jul 1, 2022	PositionSarcasm Detection	—Unverified
iCompass at Shared Task on Sarcasm and Sentiment Detection in Arabic	Apr 1, 2021	Sarcasm DetectionSentiment Analysis	—Unverified
Identifying Affective Events and the Reasons for their Polarity	Oct 1, 2018	Sarcasm DetectionWeakly-supervised Learning	—Unverified
Impact of emoji exclusion on the performance of Arabic sarcasm detection models	May 3, 2024	NavigateSarcasm Detection	—Unverified
Indonesian Social Media Sentiment Analysis With Sarcasm Detection	May 12, 2015	BIG-bench Machine LearningGeneral Classification	—Unverified
InfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks	Feb 22, 2024	Sarcasm DetectionStance Classification	—Unverified
Intermediate-Task Transfer Learning: Leveraging Sarcasm Detection for Stance Detection	Mar 5, 2025	Sarcasm DetectionStance Detection	—Unverified
Interpretable Bangla Sarcasm Detection using BERT and Explainable AI	Mar 22, 2023	Sarcasm DetectionSentence	—Unverified

Show:10 25 50

← PrevPage 16 of 27Next →

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