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 131–140 of 266 papers

Title	Date	Tasks	Status
A Survey on Automated Sarcasm Detection on Twitter	Feb 5, 2022	Sarcasm DetectionSurvey	—Unverified
Zombies Eat Brains, You are Safe: A Knowledge Infusion based Multitasking System for Sarcasm Detection in Meme	Jan 16, 2022	Emotion RecognitionSarcasm Detection	—Unverified
An Emoji-aware Multitask Framework for Multimodal Sarcasm Detection	Jan 16, 2022	Sarcasm Detection	—Unverified
Sentiment Analysis and Sarcasm Detection of Indian General Election Tweets	Jan 3, 2022	Sarcasm DetectionSentiment Analysis	—Unverified
How effective is incongruity? Implications for code-mixed sarcasm detection	Dec 1, 2021	Sarcasm Detection	CodeCode Available
A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict	Nov 16, 2021	Sarcasm Detection	—Unverified
An Investigation into the Contribution of Locally Aggregated Descriptors to Figurative Language Identification	Nov 1, 2021	Language IdentificationNatural Language Understanding	CodeCode Available
Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection	Oct 21, 2021	Humor DetectionSarcasm Detection	—Unverified
AraCOVID19-SSD: Arabic COVID-19 Sentiment and Sarcasm Detection Dataset	Oct 5, 2021	ArticlesSarcasm Detection	—Unverified
Does Commonsense help in detecting Sarcasm?	Sep 17, 2021	Language ModelingLanguage Modelling	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