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 121–130 of 266 papers

Title	Date	Tasks	Status
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
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks	Jul 1, 2020	Emotion ClassificationRepresentation Learning	—Unverified
Bootstrapped Learning of Emotion Hashtags \#hashtags4you	Jun 1, 2013	Emotion ClassificationSarcasm Detection	—Unverified
From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset	May 1, 2020	Arabic Sentiment AnalysisSarcasm Detection	—Unverified
Fracking Sarcasm using Neural Network	Jun 1, 2016	Sarcasm Detection	—Unverified
BNS-Net: A Dual-channel Sarcasm Detection Method Considering Behavior-level and Sentence-level Conflicts	Sep 7, 2023	Binary 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