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 51–100 of 266 papers

TitleStatusHype
CNN- and LSTM-based Claim Classification in Online User Comments—0
Indonesian Social Media Sentiment Analysis With Sarcasm Detection—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
A Survey of Multimodal Sarcasm Detection—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
Bi-ISCA: Bidirectional Inter-Sentence Contextual Attention Mechanism for Detecting Sarcasm in User Generated Noisy Short Text—0
InfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks—0
Interpretable Bangla Sarcasm Detection using BERT and Explainable AI—0
iCompass at Shared Task on Sarcasm and Sentiment Detection in Arabic—0
BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English—0
Identifying Affective Events and the Reasons for their Polarity—0
Benchmarking Transformer-based Language Models for Arabic Sentiment and Sarcasm Detection—0
akaBERT at SemEval-2022 Task 6: An Ensemble Transformer-based Model for Arabic Sarcasm Detection—0
I2C at SemEval-2022 Task 6: Intended Sarcasm Detection on Social Networks with Deep Learning—0
Impact of emoji exclusion on the performance of Arabic sarcasm detection models—0
Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media—0
DUCS at SemEval-2022 Task 6: Exploring Emojis and Sentiments for Sarcasm Detection—0
BanglaSarc: A Dataset for Sarcasm Detection—0
An Emoji-aware Multitask Framework for Multimodal Sarcasm Detection—0
An Evaluation of State-of-the-Art Large Language Models for Sarcasm Detection—0
High Tech team at SemEval-2022 Task 6: Intended Sarcasm Detection for Arabic texts—0
Baseline English and Maltese-English Classification Models for Subjectivity Detection, Sentiment Analysis, Emotion Analysis, Sarcasm Detection, and Irony 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
BFCAI at SemEval-2022 Task 6: Multi-Layer Perceptron for Sarcasm Detection in Arabic Texts—0
Exploring Author Context for Detecting Intended vs Perceived Sarcasm—0
Facebook Reaction-Based Emotion Classifier as Cue for Sarcasm Detection—0
FII UAIC at SemEval-2022 Task 6: iSarcasmEval - Intended Sarcasm Detection in English and Arabic—0
An LSTM-Based Deep Learning Approach for Detecting Self-Deprecating Sarcasm in Textual Data—0
CAF-I: A Collaborative Multi-Agent Framework for Enhanced Irony Detection with Large Language Models—0
Fracking Sarcasm using Neural Network—0
From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset—0
How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from English Literature—0
A Novel Hierarchical BERT Architecture for Sarcasm Detection—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
Building a Bridge: A Method for Image-Text Sarcasm Detection Without Pretraining on Image-Text Data—0
Harnessing Cognitive Features for Sarcasm Detection—0
Harnessing Context Incongruity for Sarcasm Detection—0
Automatic Sarcasm Detection: A Survey—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