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 151–160 of 266 papers

Title	Date	Tasks	Status
Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection	Apr 19, 2021	Meta-LearningSarcasm Detection	CodeCode Available
Zero-Resource Multi-Dialectal Arabic Natural Language Understanding	Apr 14, 2021	named-entity-recognitionNamed Entity Recognition	—Unverified
``Laughing at you or with you'': The Role of Sarcasm in Shaping the Disagreement Space	Apr 1, 2021	ClassificationRelation	CodeCode Available
Machine Learning-Based Model for Sentiment and Sarcasm Detection	Apr 1, 2021	BIG-bench Machine LearningSarcasm Detection	—Unverified
ArSarcasm Shared Task: An Ensemble BERT Model for SarcasmDetection in Arabic Tweets	Apr 1, 2021	Contrastive LearningSarcasm Detection	—Unverified
iCompass at Shared Task on Sarcasm and Sentiment Detection in Arabic	Apr 1, 2021	Sarcasm DetectionSentiment Analysis	—Unverified
SarcasmDet at Sarcasm Detection Task 2021 in Arabic using AraBERT Pretrained Model	Apr 1, 2021	Sarcasm Detection	—Unverified
Multi-task Learning Using a Combination of Contextualised and Static Word Embeddings for Arabic Sarcasm Detection and Sentiment Analysis	Apr 1, 2021	Multi-Task LearningNatural Language Understanding	—Unverified
The IDC System for Sentiment Classification and Sarcasm Detection in Arabic	Apr 1, 2021	Sarcasm DetectionSentiment Analysis	—Unverified
Benchmarking Transformer-based Language Models for Arabic Sentiment and Sarcasm Detection	Apr 1, 2021	BenchmarkingSarcasm 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