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	Score
CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models	May 1, 2024	Language ModelingLanguage Modelling	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
Multi-modal Sarcasm Detection and Humor Classification in Code-mixed Conversations	May 20, 2021	ClassificationMulti-modal Classification	CodeCode Available	1	5
Reactive Supervision: A New Method for Collecting Sarcasm Data	Sep 28, 2020	Sarcasm Detection	CodeCode Available	1	5
RCLMuFN: Relational Context Learning and Multiplex Fusion Network for Multimodal Sarcasm Detection	Dec 17, 2024	Sarcasm 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
Dual-level Adaptive Incongruity-enhanced Model for Multimodal Sarcasm Detection	Jan 7, 2025	Contrastive LearningSarcasm Detection	CodeCode Available	1	5
The Role of Conversation Context for Sarcasm Detection in Online Interactions	Jul 19, 2017	Sarcasm DetectionSentence	CodeCode Available	1	5
Effectiveness of Data-Driven Induction of Semantic Spaces and Traditional Classifiers for Sarcasm Detection	Apr 2, 2019	Sarcasm Detection	CodeCode Available	0	5
Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues	Nov 20, 2022	Emotion RecognitionNatural Language Understanding	CodeCode Available	0	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