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 11–20 of 266 papers

Title	Date	Tasks	Status	Hype	Score
"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	5
Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network	May 1, 2022	Sarcasm Detection	CodeCode Available	1	5
Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic	Apr 1, 2021	Sarcasm DetectionSentiment Analysis	CodeCode Available	1	5
Perceived and Intended Sarcasm Detection with Graph Attention Networks	Oct 8, 2021	Graph AttentionSarcasm Detection	CodeCode Available	1	5
A Multimodal Corpus for Emotion Recognition in Sarcasm	Jun 5, 2022	Emotion RecognitionSarcasm Detection	CodeCode Available	1	5
“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	5
Affective and Contextual Embedding for Sarcasm Detection	Dec 1, 2020	Sarcasm DetectionWord Embeddings	CodeCode Available	1	5
DIP: Dual Incongruity Perceiving Network for Sarcasm Detection	Jan 1, 2023	Sarcasm DetectionSemantic Similarity	CodeCode Available	1	5
MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction Experts	Nov 16, 2023	Binary ClassificationDescriptive	CodeCode Available	1	5
InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detection	Jun 24, 2024	Sarcasm DetectionSentiment Analysis	CodeCode Available	1	5

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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