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 151–175 of 266 papers

TitleStatusHype
Human Centered NLP with User-Factor Adaptation—0
I2C at SemEval-2022 Task 6: Intended Sarcasm in English using Deep Learning Techniques—0
I2C at SemEval-2022 Task 6: Intended Sarcasm Detection on Social Networks with Deep Learning—0
iCompass at Shared Task on Sarcasm and Sentiment Detection in Arabic—0
Identifying Affective Events and the Reasons for their Polarity—0
Impact of emoji exclusion on the performance of Arabic sarcasm detection models—0
Indonesian Social Media Sentiment Analysis With Sarcasm Detection—0
InfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks—0
Intermediate-Task Transfer Learning: Leveraging Sarcasm Detection for Stance Detection—0
Interpretable Bangla Sarcasm Detection using BERT and Explainable AI—0
Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media—0
Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing—0
iSarcasm: A Dataset of Intended Sarcasm—0
ISD at SemEval-2022 Task 6: Sarcasm Detection Using Lightweight Models—0
Is Sarcasm Detection A Step-by-Step Reasoning Process in Large Language Models?—0
JCT at SemEval-2022 Task 6-A: Sarcasm Detection in Tweets Written in English and Arabic using Preprocessing Methods and Word N-grams—0
KLUEnicorn at SemEval-2018 Task 3: A Naive Approach to Irony Detection—0
Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks—0
Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network—0
Leveraging Cognitive Features for Sentiment Analysis—0
Leveraging Large Language Models for Sarcastic Speech Annotation in Sarcasm Detection—0
Leveraging Offensive Language for Sarcasm and Sentiment Detection in Arabic—0
LISACTeam at SemEval-2022 Task 6: A Transformer based Approach for Intended Sarcasm Detection in English Tweets—0
Machine Learning-Based Model for Sentiment and Sarcasm Detection—0
MarSan at SemEval-2022 Task 6: iSarcasm Detection via T5 and Sequence Learners—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