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 161–170 of 266 papers

Title	Date	Tasks	Status
TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models	Mar 27, 2023	Humor DetectionMultimodal Sentiment Analysis	—Unverified
The IDC System for Sentiment Classification and Sarcasm Detection in Arabic	Apr 1, 2021	Sarcasm DetectionSentiment Analysis	—Unverified
The Impact of Figurative Language on Sentiment Analysis	Sep 1, 2017	Sarcasm DetectionSentiment Analysis	—Unverified
Thematic context vector association based on event uncertainty for Twitter	Apr 4, 2023	Keyword ExtractionSarcasm Detection	—Unverified
The perfect solution for detecting sarcasm in tweets \#not	Jun 1, 2013	Sarcasm DetectionSentiment Analysis	—Unverified
Token-free Models for Sarcasm Detection	May 2, 2025	Sarcasm Detection	—Unverified
Towards Code-switched Classification Exploiting Constituent Language Resources	Nov 3, 2020	ClassificationGeneral Classification	—Unverified
SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding	Aug 21, 2024	Logical ReasoningMathematical Reasoning	—Unverified
Towards Lower Bounds on Number of Dimensions for Word Embeddings	Nov 1, 2017	Named Entity Recognition (NER)Part-Of-Speech Tagging	—Unverified
Transformer-based Context-aware Sarcasm Detection in Conversation Threads from Social Media	May 22, 2020	Sarcasm Detection	—Unverified

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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