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 21–30 of 266 papers

Title	Date	Tasks	Status	Hype
Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic	Apr 1, 2021	Sarcasm DetectionSentiment Analysis	CodeCode Available	1
Affective and Contextual Embedding for Sarcasm Detection	Dec 1, 2020	Sarcasm DetectionWord Embeddings	CodeCode Available	1
“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
"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
Sarcasm Detection using Hybrid Neural Network	Aug 20, 2019	Sarcasm Detection	CodeCode Available	1
The Role of Conversation Context for Sarcasm Detection in Online Interactions	Jul 19, 2017	Sarcasm DetectionSentence	CodeCode Available	1
Modelling Context with User Embeddings for Sarcasm Detection in Social Media	Jul 4, 2016	Feature EngineeringSarcasm Detection	CodeCode Available	1
CAF-I: A Collaborative Multi-Agent Framework for Enhanced Irony Detection with Large Language Models	Jun 10, 2025	Language ModelingLanguage Modelling	—Unverified	0
Leveraging Large Language Models for Sarcastic Speech Annotation in Sarcasm Detection	Jun 1, 2025	Sarcasm Detection	—Unverified	0

Show:10 25 50

← PrevPage 3 of 27Next →

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