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 11–20 of 266 papers

Title	Date	Tasks	Status	Hype
Evaluating Large Language Models Against Human Annotators in Latent Content Analysis: Sentiment, Political Leaning, Emotional Intensity, and Sarcasm	Jan 5, 2025	Sarcasm DetectionSentiment Analysis	—Unverified	0
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning	Dec 17, 2024	Contrastive LearningSarcasm Detection	—Unverified	0
RCLMuFN: Relational Context Learning and Multiplex Fusion Network for Multimodal Sarcasm Detection	Dec 17, 2024	Sarcasm Detection	CodeCode Available	1
Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection	Dec 17, 2024	Multi-Task LearningSarcasm Detection	—Unverified	0
AMuSeD: An Attentive Deep Neural Network for Multimodal Sarcasm Detection Incorporating Bi-modal Data Augmentation	Dec 13, 2024	Data AugmentationSarcasm Detection	—Unverified	0
BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English	Dec 6, 2024	Sarcasm DetectionSentiment Analysis	—Unverified	0
Pragmatic Metacognitive Prompting Improves LLM Performance on Sarcasm Detection	Dec 4, 2024	Sarcasm DetectionSentiment Analysis	—Unverified	0
Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks	Dec 4, 2024	ClassificationContrastive Learning	—Unverified	0
Multi-View Incongruity Learning for Multimodal Sarcasm Detection	Dec 1, 2024	Contrastive LearningData Augmentation	—Unverified	0
Was that Sarcasm?: A Literature Survey on Sarcasm Detection	Nov 30, 2024	Sarcasm DetectionSurvey	—Unverified	0

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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