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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 101–125 of 266 papers

Title	Date	Tasks	Status	Hype
LT3 at SemEval-2022 Task 6: Fuzzy-Rough Nearest Neighbor Classification for Sarcasm Detection	Jul 1, 2022	Binary ClassificationClassification	CodeCode Available	0
PALI-NLP at SemEval-2022 Task 6: iSarcasmEval- Fine-tuning the Pre-trained Model for Detecting Intended Sarcasm	Jul 1, 2022	Sarcasm Detection	—Unverified	0
Plumeria at SemEval-2022 Task 6: Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation	Jul 1, 2022	Data AugmentationFeature Engineering	—Unverified	0
Dartmouth at SemEval-2022 Task 6: Detection of Sarcasm	Jul 1, 2022	Data AugmentationSarcasm Detection	—Unverified	0
I2C at SemEval-2022 Task 6: Intended Sarcasm Detection on Social Networks with Deep Learning	Jul 1, 2022	PositionSarcasm Detection	—Unverified	0
Understanding the Sarcastic Nature of Emojis with SarcOji	Jul 1, 2022	PositionSarcasm Detection	—Unverified	0
muBoost: An Effective Method for Solving Indic Multilingual Text Classification Problem	Jun 21, 2022	Multilingual text classificationSarcasm Detection	—Unverified	0
CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic	Jun 16, 2022	Author ProfilingOpinion Mining	CodeCode Available	0
A Multimodal Corpus for Emotion Recognition in Sarcasm	Jun 5, 2022	Emotion RecognitionSarcasm Detection	CodeCode Available	1
Baseline English and Maltese-English Classification Models for Subjectivity Detection, Sentiment Analysis, Emotion Analysis, Sarcasm Detection, and Irony Detection	Jun 1, 2022	ClassificationEmotion Recognition	—Unverified	0
BFCAI at SemEval-2022 Task 6: Multi-Layer Perceptron for Sarcasm Detection in Arabic Texts	May 18, 2022	Sarcasm Detection	—Unverified	0
Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource Language	May 2, 2022	Graph ReconstructionSarcasm Detection	CodeCode Available	0
Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network	May 1, 2022	Sarcasm Detection	CodeCode Available	1
UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection Using Generative-based and Mutation-based Data Augmentation	Apr 18, 2022	Data AugmentationSarcasm Detection	CodeCode Available	1
Training Compute-Optimal Large Language Models	Mar 29, 2022	AnachronismsAnalogical Similarity	CodeCode Available	6
When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues	Mar 12, 2022	Sarcasm Detection	CodeCode Available	1
Plumeria at SemEval-2022 Task 6: Robust Approaches for Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation	Mar 8, 2022	Data AugmentationFeature Engineering	CodeCode Available	0
How Effective is Incongruity? Implications for Code-mix Sarcasm Detection	Feb 6, 2022	Sarcasm Detection	CodeCode Available	0
A Survey on Automated Sarcasm Detection on Twitter	Feb 5, 2022	Sarcasm DetectionSurvey	—Unverified	0
An Emoji-aware Multitask Framework for Multimodal Sarcasm Detection	Jan 16, 2022	Sarcasm Detection	—Unverified	0
Zombies Eat Brains, You are Safe: A Knowledge Infusion based Multitasking System for Sarcasm Detection in Meme	Jan 16, 2022	Emotion RecognitionSarcasm Detection	—Unverified	0
Sentiment Analysis and Sarcasm Detection of Indian General Election Tweets	Jan 3, 2022	Sarcasm DetectionSentiment Analysis	—Unverified	0
Scaling Language Models: Methods, Analysis & Insights from Training Gopher	Dec 8, 2021	Abstract AlgebraAnachronisms	CodeCode Available	2
How effective is incongruity? Implications for code-mixed sarcasm detection	Dec 1, 2021	Sarcasm Detection	CodeCode Available	0
A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict	Nov 16, 2021	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