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

Showing 251266 of 266 papers

TitleStatusHype
Facebook Reaction-Based Emotion Classifier as Cue for Sarcasm Detection0
FII UAIC at SemEval-2022 Task 6: iSarcasmEval - Intended Sarcasm Detection in English and Arabic0
Fracking Sarcasm using Neural Network0
From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset0
Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science0
GetSmartMSEC at SemEval-2022 Task 6: Sarcasm Detection using Contextual Word Embedding with Gaussian model for Irony Type Identification0
Harnessing Cognitive Features for Sarcasm Detection0
Harnessing Context Incongruity for Sarcasm Detection0
Harnessing Sequence Labeling for Sarcasm Detection in Dialogue from TV Series `Friends'0
"Having 2 hours to write a paper is fun!": Detecting Sarcasm in Numerical Portions of Text0
High Tech team at SemEval-2022 Task 6: Intended Sarcasm Detection for Arabic texts0
How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from English Literature0
Human Centered NLP with User-Factor Adaptation0
I2C at SemEval-2022 Task 6: Intended Sarcasm in English using Deep Learning Techniques0
I2C at SemEval-2022 Task 6: Intended Sarcasm Detection on Social Networks with Deep Learning0
iCompass at Shared Task on Sarcasm and Sentiment Detection in Arabic0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PaLM 2(few-shot, k=3, CoT)Accuracy84.8Unverified
2PaLM 2 (few-shot, k=3, Direct)Accuracy78.7Unverified
3PaLM 540B (few-shot, k=3)Accuracy78.1Unverified
4BLOOM 176B (few-shot, k=3)Accuracy72.47Unverified
5Bloomberg GPT (few-shot, k=3)Accuracy69.66Unverified
6GPT-NeoX (few-shot, k=3)Accuracy62.36Unverified
7Chinchilla-70B (few-shot, k=5)Accuracy58.6Unverified
8Gopher-280B (few-shot, k=5)Accuracy48.3Unverified
#ModelMetricClaimedVerifiedStatus
1BERT+Aspect-based approachesF10.74Unverified
2RoBERTa_large - (Separated Context-Response)F10.72Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa_large (Context-Response)F10.77Unverified
2BERTF10.73Unverified
#ModelMetricClaimedVerifiedStatus
1CASCADEAccuracy77Unverified
2Bag-of-BigramsAccuracy75.8Unverified
#ModelMetricClaimedVerifiedStatus
1Bag-of-BigramsAccuracy76.5Unverified
2CASCADEAccuracy74Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa + Mutation Data AugmentationF1-Score0.41Unverified
#ModelMetricClaimedVerifiedStatus
1MUStARD++Precision70.2Unverified
#ModelMetricClaimedVerifiedStatus
1Bag-of-WordsAvg F127Unverified
#ModelMetricClaimedVerifiedStatus
1BARTR136.88Unverified