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 221–230 of 266 papers

Title	Date	Tasks	Status
Sarcasm Target Identification: Dataset and An Introductory Approach	May 1, 2018	Sarcasm DetectionSentiment Analysis	CodeCode Available
SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model	Dec 1, 2017	General ClassificationMulti-Label Classification	—Unverified
Towards Lower Bounds on Number of Dimensions for Word Embeddings	Nov 1, 2017	Named Entity Recognition (NER)Part-Of-Speech Tagging	—Unverified
"Having 2 hours to write a paper is fun!": Detecting Sarcasm in Numerical Portions of Text	Sep 6, 2017	BIG-bench Machine LearningDeep Learning	—Unverified
Computational Sarcasm	Sep 1, 2017	Sarcasm DetectionSentiment Analysis	—Unverified
Cross-lingual Flames Detection in News Discussions	Sep 1, 2017	Sarcasm DetectionSentiment Analysis	—Unverified
The Impact of Figurative Language on Sentiment Analysis	Sep 1, 2017	Sarcasm DetectionSentiment Analysis	—Unverified
Detecting Sarcasm Using Different Forms Of Incongruity	Sep 1, 2017	Sarcasm DetectionSentiment Analysis	—Unverified
Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal	Sep 1, 2017	Sarcasm Detection	CodeCode Available
Human Centered NLP with User-Factor Adaptation	Sep 1, 2017	Document ClassificationDomain Adaptation	—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