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 226–250 of 266 papers

TitleStatusHype
C-Net: Contextual Network for Sarcasm Detection—0
CNN- and LSTM-based Claim Classification in Online User Comments—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
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
Dartmouth at SemEval-2022 Task 6: Detection of Sarcasm—0
Debiasing Multimodal Sarcasm Detection with Contrastive Learning—0
Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language—0
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning—0
Detecting Sarcasm in Conversation Context Using Transformer-Based Models—0
Detecting Sarcasm is Extremely Easy ;-)—0
Detecting Sarcasm Using Different Forms Of Incongruity—0
DUCS at SemEval-2022 Task 6: Exploring Emojis and Sentiments for Sarcasm Detection—0
Emotion Enriched Retrofitted Word Embeddings—0
Enhancing Multimodal Affective Analysis with Learned Live Comment Features—0
Evaluating Large Language Models Against Human Annotators in Latent Content Analysis: Sentiment, Political Leaning, Emotional Intensity, and Sarcasm—0
Expect the unexpected: Harnessing Sentence Completion for Sarcasm Detection—0
Explainable Detection of Sarcasm in Social Media—0
Exploring Author Context for Detecting Intended vs Perceived Sarcasm—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