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 151–175 of 266 papers

Title	Date	Tasks	Status	Hype
iCompass at Shared Task on Sarcasm and Sentiment Detection in Arabic	Apr 1, 2021	Sarcasm DetectionSentiment Analysis	—Unverified	0
ArSarcasm Shared Task: An Ensemble BERT Model for SarcasmDetection in Arabic Tweets	Apr 1, 2021	Contrastive LearningSarcasm Detection	—Unverified	0
Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation	Apr 1, 2021	Data AugmentationSarcasm Detection	—Unverified	0
The IDC System for Sentiment Classification and Sarcasm Detection in Arabic	Apr 1, 2021	Sarcasm DetectionSentiment Analysis	—Unverified	0
Benchmarking Transformer-based Language Models for Arabic Sentiment and Sarcasm Detection	Apr 1, 2021	BenchmarkingSarcasm Detection	—Unverified	0
Machine Learning-Based Model for Sentiment and Sarcasm Detection	Apr 1, 2021	BIG-bench Machine LearningSarcasm Detection	—Unverified	0
Leveraging Offensive Language for Sarcasm and Sentiment Detection in Arabic	Apr 1, 2021	Sarcasm DetectionSentiment Analysis	—Unverified	0
``Laughing at you or with you'': The Role of Sarcasm in Shaping the Disagreement Space	Apr 1, 2021	ClassificationRelation	CodeCode Available	0
Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment Identification	Mar 9, 2021	Language ModelingLanguage Modelling	—Unverified	0
"Laughing at you or with you": The Role of Sarcasm in Shaping the Disagreement Space	Jan 26, 2021	ClassificationGeneral Classification	CodeCode Available	0
Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media	Jan 14, 2021	Sarcasm Detection	—Unverified	0
Affective and Contextual Embedding for Sarcasm Detection	Dec 1, 2020	Sarcasm DetectionWord Embeddings	CodeCode Available	1
All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on Memes	Dec 1, 2020	AllMulti-Task Learning	—Unverified	0
Bi-ISCA: Bidirectional Inter-Sentence Contextual Attention Mechanism for Detecting Sarcasm in User Generated Noisy Short Text	Nov 23, 2020	Sarcasm DetectionSentence	—Unverified	0
Towards Code-switched Classification Exploiting Constituent Language Resources	Nov 3, 2020	ClassificationGeneral Classification	—Unverified	0
Building a Bridge: A Method for Image-Text Sarcasm Detection Without Pretraining on Image-Text Data	Nov 1, 2020	Sarcasm Detection	—Unverified	0
“Did you really mean what you said?” : Sarcasm Detection in Hindi-English Code-Mixed Data using Bilingual Word Embeddings	Nov 1, 2020	Deep LearningSarcasm Detection	CodeCode Available	1
Modeling Intra and Inter-modality Incongruity for Multi-Modal Sarcasm Detection	Nov 1, 2020	Sarcasm Detection	—Unverified	0
"Did you really mean what you said?" : Sarcasm Detection in Hindi-English Code-Mixed Data using Bilingual Word Embeddings	Oct 1, 2020	Deep LearningSarcasm Detection	CodeCode Available	1
Reactive Supervision: A New Method for Collecting Sarcasm Data	Sep 28, 2020	Sarcasm Detection	CodeCode Available	1
Transformers on Sarcasm Detection with Context	Jul 1, 2020	ClassificationGeneral Classification	—Unverified	0
A Novel Hierarchical BERT Architecture for Sarcasm Detection	Jul 1, 2020	Sarcasm DetectionSentence	—Unverified	0
Detecting Sarcasm in Conversation Context Using Transformer-Based Models	Jul 1, 2020	Sarcasm DetectionSentence	—Unverified	0
Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm Detection	Jul 1, 2020	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified	0
Neural Sarcasm Detection using Conversation Context	Jul 1, 2020	Sarcasm Detection	—Unverified	0

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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