SOTAVerified

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 51–75 of 266 papers

TitleStatusHype
CNN- and LSTM-based Claim Classification in Online User Comments—0
An LSTM-Based Deep Learning Approach for Detecting Self-Deprecating Sarcasm in Textual Data—0
Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment Identification—0
Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models—0
Computational Sarcasm—0
Computational Sarcasm Analysis on Social Media: A Systematic Review—0
connotation_clashers at SemEval-2022 Task 6: The effect of sentiment analysis on sarcasm detection—0
Context-Aware Sarcasm Detection Using BERT—0
A Survey of Multimodal Sarcasm Detection—0
Cross-Cultural Transfer Learning for Text Classification—0
Cross-lingual Flames Detection in News Discussions—0
CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification—0
Bi-ISCA: Bidirectional Inter-Sentence Contextual Attention Mechanism for Detecting Sarcasm in User Generated Noisy Short Text—0
CAF-I: A Collaborative Multi-Agent Framework for Enhanced Irony Detection with Large Language Models—0
Explainable Detection of Sarcasm in Social Media—0
BFCAI at SemEval-2022 Task 6: Multi-Layer Perceptron for Sarcasm Detection in Arabic Texts—0
BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English—0
Evaluating Large Language Models Against Human Annotators in Latent Content Analysis: Sentiment, Political Leaning, Emotional Intensity, and Sarcasm—0
Benchmarking Transformer-based Language Models for Arabic Sentiment and Sarcasm Detection—0
Baseline English and Maltese-English Classification Models for Subjectivity Detection, Sentiment Analysis, Emotion Analysis, Sarcasm Detection, and Irony Detection—0
akaBERT at SemEval-2022 Task 6: An Ensemble Transformer-based Model for Arabic Sarcasm Detection—0
Expect the unexpected: Harnessing Sentence Completion for Sarcasm Detection—0
Exploring Author Context for Detecting Intended vs Perceived Sarcasm—0
BanglaSarc: A Dataset for Sarcasm Detection—0
An Evaluation of State-of-the-Art Large Language Models for Sarcasm Detection—0
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All datasetsBIG-bench (SNARKS)FigLang 2020 Reddit DatasetFigLang 2020 Twitter DatasetSARC (all-bal)SARC (pol-bal)iSarcasmMUStARDSARC (pol-unbal)WITS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PaLM 2(few-shot, k=3, CoT)Accuracy84.8—Unverified
2PaLM 2 (few-shot, k=3, Direct)Accuracy78.7—Unverified
3PaLM 540B (few-shot, k=3)Accuracy78.1—Unverified
4BLOOM 176B (few-shot, k=3)Accuracy72.47—Unverified
5Bloomberg GPT (few-shot, k=3)Accuracy69.66—Unverified
6GPT-NeoX (few-shot, k=3)Accuracy62.36—Unverified
7Chinchilla-70B (few-shot, k=5)Accuracy58.6—Unverified
8Gopher-280B (few-shot, k=5)Accuracy48.3—Unverified
#ModelMetricClaimedVerifiedStatus
1BERT+Aspect-based approachesF10.74—Unverified
2RoBERTa_large - (Separated Context-Response)F10.72—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa_large (Context-Response)F10.77—Unverified
2BERTF10.73—Unverified
#ModelMetricClaimedVerifiedStatus
1CASCADEAccuracy77—Unverified
2Bag-of-BigramsAccuracy75.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Bag-of-BigramsAccuracy76.5—Unverified
2CASCADEAccuracy74—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa + Mutation Data AugmentationF1-Score0.41—Unverified
#ModelMetricClaimedVerifiedStatus
1MUStARD++Precision70.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Bag-of-WordsAvg F127—Unverified
#ModelMetricClaimedVerifiedStatus
1BARTR136.88—Unverified