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 231–240 of 266 papers

Title	Date	Tasks	Status	Hype
CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification	Aug 1, 2017	General ClassificationOpinion Mining	—Unverified	0
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm	Aug 1, 2017	DiversitySarcasm Detection	CodeCode Available	0
The Role of Conversation Context for Sarcasm Detection in Online Interactions	Jul 19, 2017	Sarcasm DetectionSentence	CodeCode Available	1
Expect the unexpected: Harnessing Sentence Completion for Sarcasm Detection	Jul 19, 2017	Sarcasm DetectionSentence	—Unverified	0
Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network	Jul 1, 2017	EEGElectroencephalogram (EEG)	—Unverified	0
Context-Dependent Sentiment Analysis in User-Generated Videos	Jul 1, 2017	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	0
A Large Self-Annotated Corpus for Sarcasm	Apr 19, 2017	Sarcasm Detection	CodeCode Available	0
Leveraging Cognitive Features for Sentiment Analysis	Jan 19, 2017	General ClassificationSarcasm Detection	—Unverified	0
Harnessing Cognitive Features for Sarcasm Detection	Jan 19, 2017	Sarcasm DetectionSentence	—Unverified	0
How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from English Literature	Dec 1, 2016	General ClassificationSarcasm Detection	—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