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 211–220 of 266 papers

Title	Date	Tasks	Status
Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation	Apr 1, 2021	Data AugmentationSarcasm Detection	—Unverified
Sarcasm and Sentiment Detection in Arabic: investigating the interest of character-level features	Apr 1, 2021	Sarcasm Detection	—Unverified
SarcasmDet at Sarcasm Detection Task 2021 in Arabic using AraBERT Pretrained Model	Apr 1, 2021	Sarcasm Detection	—Unverified
SarcasmDet at SemEval-2022 Task 6: Detecting Sarcasm using Pre-trained Transformers in English and Arabic Languages	Jul 1, 2022	Sarcasm Detection	—Unverified
Sarcasm Detection: A Comparative Study	Jul 5, 2021	Sarcasm DetectionSentiment Analysis	—Unverified
Sarcasm Detection and Building an English Language Corpus in Real Time	Sep 1, 2021	Sarcasm Detection	—Unverified
sarcasm detection and quantification in arabic tweets	Aug 3, 2021	Binary ClassificationSarcasm Detection	—Unverified
Gender Bias Mitigation for Bangla Classification Tasks	Nov 16, 2024	ClassificationHate Speech Detection	CodeCode Available
Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!	Apr 9, 2024	Sarcasm Detection	CodeCode Available
A big data approach towards sarcasm detection in Russian	Jun 1, 2023	Sarcasm Detection	CodeCode Available

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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