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 141–150 of 266 papers

Title	Date	Tasks	Status
Sarcasm Detection as a Catalyst: Improving Stance Detection with Cross-Target Capabilities	Mar 5, 2025	Sarcasm DetectionStance Detection	—Unverified
Sarcasm Detection : Building a Contextual Hierarchy	Dec 1, 2016	Lexical AnalysisSarcasm Detection	—Unverified
Sarcasm Detection Framework Using Context, Emotion and Sentiment Features	Nov 23, 2022	Sarcasm DetectionSentiment Analysis	—Unverified
Sarcasm Detection in Chinese Using a Crowdsourced Corpus	Oct 1, 2016	Sarcasm DetectionSentiment Analysis	—Unverified
Sarcasm Detection in Tweets with BERT and GloVe Embeddings	Jun 20, 2020	BIG-bench Machine LearningSarcasm Detection	—Unverified
#SarcasmDetection is soooo general! Towards a Domain-Independent Approach for Detecting Sarcasm	Jun 8, 2018	Domain AdaptationSarcasm Detection	—Unverified
Sarcasm Detection on Czech and English Twitter	Aug 1, 2014	Sarcasm DetectionSentiment Analysis	—Unverified
Sarcasm Detection Using an Ensemble Approach	Jul 1, 2020	Sarcasm Detection	—Unverified
Sarcasm Detection using Context Separators in Online Discourse	Jun 1, 2020	Sarcasm Detection	—Unverified
Sarcasm Identification and Detection in Conversion Context using BERT	Jul 1, 2020	Sarcasm Detection	—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