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 126–150 of 266 papers

TitleStatusHype
TUG-CIC at SemEval-2021 Task 6: Two-stage Fine-tuning for Intended Sarcasm Detection—0
Understanding the Sarcastic Nature of Emojis with SarcOji—0
UoR-NCL at SemEval-2022 Task 6: Using ensemble loss with BERT for intended sarcasm detection—0
Urban Dictionary Embeddings for Slang NLP Applications—0
ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm—0
Was that Sarcasm?: A Literature Survey on Sarcasm Detection—0
``When Numbers Matter!'': Detecting Sarcasm in Numerical Portions of Text—0
Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis.—0
`Who would have thought of that!': A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection—0
Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining—0
X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detection—0
YNU-HPCC at SemEval-2018 Task 3: Ensemble Neural Network Models for Irony Detection on Twitter—0
YNU-HPCC at SemEval-2022 Task 6: Transformer-based Model for Intended Sarcasm Detection in English and Arabic—0
Your Sentiment Precedes You: Using an author's historical tweets to predict sarcasm—0
YouTube Comments Decoded: Leveraging LLMs for Low Resource Language Classification—0
Zero-Resource Multi-Dialectal Arabic Natural Language Understanding—0
CAF-I: A Collaborative Multi-Agent Framework for Enhanced Irony Detection with Large Language Models—0
Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science—0
GetSmartMSEC at SemEval-2022 Task 6: Sarcasm Detection using Contextual Word Embedding with Gaussian model for Irony Type Identification—0
Harnessing Cognitive Features for Sarcasm Detection—0
Harnessing Context Incongruity for Sarcasm Detection—0
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 Text—0
High Tech team at SemEval-2022 Task 6: Intended Sarcasm Detection for Arabic texts—0
How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from English Literature—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