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

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Showing 241–250 of 266 papers

Title	Date	Tasks	Status
Detecting Sarcasm in Conversation Context Using Transformer-Based Models	Jul 1, 2020	Sarcasm DetectionSentence	—Unverified
Detecting Sarcasm is Extremely Easy ;-)	Jun 1, 2018	Sarcasm Detection	—Unverified
Detecting Sarcasm Using Different Forms Of Incongruity	Sep 1, 2017	Sarcasm DetectionSentiment Analysis	—Unverified
DUCS at SemEval-2022 Task 6: Exploring Emojis and Sentiments for Sarcasm Detection	Jul 1, 2022	Sarcasm Detection	—Unverified
Emotion Enriched Retrofitted Word Embeddings	Oct 1, 2022	Sarcasm DetectionSentiment Analysis	—Unverified
Enhancing Multimodal Affective Analysis with Learned Live Comment Features	Oct 21, 2024	Contrastive LearningEmotion Recognition	—Unverified
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
Expect the unexpected: Harnessing Sentence Completion for Sarcasm Detection	Jul 19, 2017	Sarcasm DetectionSentence	—Unverified
Explainable Detection of Sarcasm in Social Media	Apr 1, 2021	Sarcasm Detection	—Unverified
Exploring Author Context for Detecting Intended vs Perceived Sarcasm	Oct 25, 2019	Sarcasm DetectionTAG	—Unverified

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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