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 91–100 of 266 papers

Title	Date	Tasks	Status
How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from English Literature	Dec 1, 2016	General ClassificationSarcasm Detection	—Unverified
From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset	May 1, 2020	Arabic Sentiment AnalysisSarcasm Detection	—Unverified
Bootstrapped Learning of Emotion Hashtags \#hashtags4you	Jun 1, 2013	Emotion ClassificationSarcasm Detection	—Unverified
A Novel Hierarchical BERT Architecture for Sarcasm Detection	Jul 1, 2020	Sarcasm DetectionSentence	—Unverified
Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science	May 24, 2023	Sarcasm DetectionSynthetic Data Generation	—Unverified
GetSmartMSEC at SemEval-2022 Task 6: Sarcasm Detection using Contextual Word Embedding with Gaussian model for Irony Type Identification	Jul 1, 2022	Author ProfilingOpinion Mining	—Unverified
Building a Bridge: A Method for Image-Text Sarcasm Detection Without Pretraining on Image-Text Data	Nov 1, 2020	Sarcasm Detection	—Unverified
Harnessing Cognitive Features for Sarcasm Detection	Jan 19, 2017	Sarcasm DetectionSentence	—Unverified
Harnessing Context Incongruity for Sarcasm Detection	Jul 1, 2015	Sarcasm Detection	—Unverified
Automatic Sarcasm Detection: A Survey	Feb 10, 2016	Sarcasm DetectionSentiment Analysis	—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