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 251–260 of 266 papers

Title	Date	Tasks	Status
Sarcasm Detection in Chinese Using a Crowdsourced Corpus	Oct 1, 2016	Sarcasm DetectionSentiment Analysis	—Unverified
Putting Sarcasm Detection into Context: The Effects of Class Imbalance and Manual Labelling on Supervised Machine Classification of Twitter Conversations	Aug 1, 2016	Anomaly DetectionGeneral Classification	—Unverified
Harnessing Sequence Labeling for Sarcasm Detection in Dialogue from TV Series `Friends'	Aug 1, 2016	Sarcasm DetectionSentiment Analysis	—Unverified
Fracking Sarcasm using Neural Network	Jun 1, 2016	Sarcasm Detection	—Unverified
Automatic Sarcasm Detection: A Survey	Feb 10, 2016	Sarcasm DetectionSentiment Analysis	—Unverified
Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words	Sep 1, 2015	Sarcasm DetectionWord Embeddings	—Unverified
Your Sentiment Precedes You: Using an author's historical tweets to predict sarcasm	Sep 1, 2015	Sarcasm Detection	—Unverified
Harnessing Context Incongruity for Sarcasm Detection	Jul 1, 2015	Sarcasm Detection	—Unverified
ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm	Jun 1, 2015	Opinion MiningSarcasm Detection	—Unverified
Indonesian Social Media Sentiment Analysis With Sarcasm Detection	May 12, 2015	BIG-bench Machine LearningGeneral Classification	—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