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
Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media	Jan 14, 2021	Sarcasm Detection	—Unverified
Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing	May 1, 2012	Opinion MiningSarcasm Detection	—Unverified
iSarcasm: A Dataset of Intended Sarcasm	Nov 8, 2019	Sarcasm Detection	—Unverified
ISD at SemEval-2022 Task 6: Sarcasm Detection Using Lightweight Models	Jul 1, 2022	Sarcasm DetectionSentiment Analysis	—Unverified
Is Sarcasm Detection A Step-by-Step Reasoning Process in Large Language Models?	Jul 17, 2024	BenchmarkingSarcasm Detection	—Unverified
JCT at SemEval-2022 Task 6-A: Sarcasm Detection in Tweets Written in English and Arabic using Preprocessing Methods and Word N-grams	Jul 1, 2022	Binary ClassificationSarcasm Detection	—Unverified
KLUEnicorn at SemEval-2018 Task 3: A Naive Approach to Irony Detection	Jun 1, 2018	Sarcasm Detection	—Unverified
Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks	Dec 4, 2024	ClassificationContrastive Learning	—Unverified
Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network	Jul 1, 2017	EEGElectroencephalogram (EEG)	—Unverified
Leveraging Cognitive Features for Sentiment Analysis	Jan 19, 2017	General ClassificationSarcasm 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