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
News Headlines Dataset For Sarcasm Detection	Sep 17, 2022	ArticlesSarcasm Detection	CodeCode Available	1
Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classification	Sep 15, 2022	Abstract Meaning RepresentationFake News Detection	CodeCode Available	1
SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic	Jul 1, 2022	Sarcasm DetectionSentence	CodeCode Available	1
A Multimodal Corpus for Emotion Recognition in Sarcasm	Jun 5, 2022	Emotion RecognitionSarcasm Detection	CodeCode Available	1
Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network	May 1, 2022	Sarcasm Detection	CodeCode Available	1
UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection Using Generative-based and Mutation-based Data Augmentation	Apr 18, 2022	Data AugmentationSarcasm Detection	CodeCode Available	1
When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues	Mar 12, 2022	Sarcasm Detection	CodeCode Available	1
Perceived and Intended Sarcasm Detection with Graph Attention Networks	Oct 8, 2021	Graph AttentionSarcasm Detection	CodeCode Available	1
Multi-Modal Sarcasm Detection Based on Contrastive Attention Mechanism	Sep 30, 2021	Sarcasm Detection	CodeCode Available	1
Multi-modal Sarcasm Detection and Humor Classification in Code-mixed Conversations	May 20, 2021	ClassificationMulti-modal Classification	CodeCode Available	1

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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