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
Sarcasm Detection in Chinese Using a Crowdsourced Corpus0
Putting Sarcasm Detection into Context: The Effects of Class Imbalance and Manual Labelling on Supervised Machine Classification of Twitter Conversations0
Harnessing Sequence Labeling for Sarcasm Detection in Dialogue from TV Series `Friends'0
Fracking Sarcasm using Neural Network0
Automatic Sarcasm Detection: A Survey0
Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words0
Your Sentiment Precedes You: Using an author's historical tweets to predict sarcasm0
Harnessing Context Incongruity for Sarcasm Detection0
ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm0
Indonesian Social Media Sentiment Analysis With Sarcasm Detection0
Sarcasm Detection on Czech and English Twitter0
Modelling Sarcasm in Twitter, a Novel Approach0
Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis.0
The perfect solution for detecting sarcasm in tweets \#not0
Bootstrapped Learning of Emotion Hashtags \#hashtags4you0
Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing0
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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