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 51–60 of 266 papers

Title	Date	Tasks	Status	Hype
A Wide Evaluation of ChatGPT on Affective Computing Tasks	Aug 26, 2023	Aspect ExtractionSarcasm Detection	CodeCode Available	0
Sarcasm Detection in a Disaster Context	Aug 16, 2023	Natural Language UnderstandingSarcasm Detection	CodeCode Available	0
MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System	Jul 14, 2023	Sarcasm Detection	CodeCode Available	1
A big data approach towards sarcasm detection in Russian	Jun 1, 2023	Sarcasm Detection	CodeCode Available	0
Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science	May 24, 2023	Sarcasm DetectionSynthetic Data Generation	—Unverified	0
Researchers eye-view of sarcasm detection in social media textual content	Apr 17, 2023	Sarcasm Detection	—Unverified	0
Polarity based Sarcasm Detection using Semigraph	Apr 4, 2023	Sarcasm DetectionSentiment Analysis	—Unverified	0
Thematic context vector association based on event uncertainty for Twitter	Apr 4, 2023	Keyword ExtractionSarcasm Detection	—Unverified	0
BloombergGPT: A Large Language Model for Finance	Mar 30, 2023	Causal JudgmentCommon Sense Reasoning	CodeCode Available	0
TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models	Mar 27, 2023	Humor DetectionMultimodal Sentiment Analysis	—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