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 51–75 of 266 papers

Title	Date	Tasks	Status	Score
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks	Oct 27, 2016	Sarcasm DetectionSentence	CodeCode Available	5
Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal	Sep 1, 2017	Sarcasm Detection	CodeCode Available	5
A Wide Evaluation of ChatGPT on Affective Computing Tasks	Aug 26, 2023	Aspect ExtractionSarcasm Detection	CodeCode Available	5
IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detection	May 22, 2025	In-Context LearningSarcasm Detection	CodeCode Available	5
How effective is incongruity? Implications for code-mixed sarcasm detection	Dec 1, 2021	Sarcasm Detection	CodeCode Available	5
Robust Gram Embeddings	Nov 1, 2016	Sarcasm DetectionSentiment Analysis	CodeCode Available	5
How Effective is Incongruity? Implications for Code-mix Sarcasm Detection	Feb 6, 2022	Sarcasm Detection	CodeCode Available	5
Sarcasm Detection in Twitter -- Performance Impact while using Data Augmentation: Word Embeddings	Aug 23, 2021	Data AugmentationOpinion Mining	CodeCode Available	5
Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary Tasks	Sep 14, 2023	Image-text matchingSarcasm Detection	CodeCode Available	5
KoCoSa: Korean Context-aware Sarcasm Detection Dataset	Feb 22, 2024	Dataset GenerationSarcasm Detection	CodeCode Available	5
TEDB System Description to a Shared Task on Euphemism Detection 2022	Jan 16, 2023	Sarcasm DetectionSentiment Analysis	CodeCode Available	5
Gender Bias Mitigation for Bangla Classification Tasks	Nov 16, 2024	ClassificationHate Speech Detection	CodeCode Available	5
Deep and Dense Sarcasm Detection	Nov 18, 2019	Sarcasm Detection	CodeCode Available	5
Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues	Nov 20, 2022	Emotion RecognitionNatural Language Understanding	CodeCode Available	5
Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!	Apr 9, 2024	Sarcasm Detection	CodeCode Available	5
Does Commonsense help in detecting Sarcasm?	Sep 17, 2021	Language ModelingLanguage Modelling	CodeCode Available	5
A Transformer-based approach to Irony and Sarcasm detection	Nov 23, 2019	Sarcasm DetectionSentiment Analysis	CodeCode Available	5
A big data approach towards sarcasm detection in Russian	Jun 1, 2023	Sarcasm Detection	CodeCode Available	5
FiLMing Multimodal Sarcasm Detection with Attention	Aug 9, 2021	Opinion MiningSarcasm Detection	CodeCode Available	5
Finetuning for Sarcasm Detection with a Pruned Dataset	Dec 23, 2022	Sarcasm Detection	CodeCode Available	5
CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic	Jun 16, 2022	Author ProfilingOpinion Mining	CodeCode Available	5
DocMSU: A Comprehensive Benchmark for Document-level Multimodal Sarcasm Understanding	Dec 26, 2023	Object DetectionSarcasm Detection	CodeCode Available	5
Effectiveness of Data-Driven Induction of Semantic Spaces and Traditional Classifiers for Sarcasm Detection	Apr 2, 2019	Sarcasm Detection	CodeCode Available	5
Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour	May 25, 2020	Multi-Task LearningNamed Entity Recognition (NER)	CodeCode Available	5
Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection	Apr 19, 2021	Meta-LearningSarcasm Detection	CodeCode Available	5

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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