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 76–100 of 266 papers

TitleStatusHype
Does Commonsense help in detecting Sarcasm?Code0
Multi-modal Semantic Understanding with Contrastive Cross-modal Feature AlignmentCode0
Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)Code0
CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification—0
Cross-lingual Flames Detection in News Discussions—0
A Survey on Automated Sarcasm Detection on Twitter—0
Cross-Cultural Transfer Learning for Text Classification—0
A Survey of Multimodal Sarcasm Detection—0
Amrita_CEN at SemEval-2022 Task 6: A Machine Learning Approach for Detecting Intended Sarcasm using Oversampling—0
Context-Aware Sarcasm Detection Using BERT—0
connotation_clashers at SemEval-2022 Task 6: The effect of sentiment analysis on sarcasm detection—0
Assessing how hyperparameters impact Large Language Models' sarcasm detection performance—0
Computational Sarcasm Analysis on Social Media: A Systematic Review—0
Computational Sarcasm—0
ArSarcasm Shared Task: An Ensemble BERT Model for SarcasmDetection in Arabic Tweets—0
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis—0
Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models—0
Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment Identification—0
Are Word Embedding-based Features Useful for Sarcasm Detection?—0
CNN- and LSTM-based Claim Classification in Online User Comments—0
A Report on the 2020 Sarcasm Detection Shared Task—0
C-Net: Contextual Network for Sarcasm Detection—0
Harnessing Context Incongruity for Sarcasm Detection—0
AraCOVID19-SSD: Arabic COVID-19 Sentiment and Sarcasm Detection Dataset—0
Harnessing Cognitive Features 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