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 176–200 of 266 papers

TitleStatusHype
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
Zombies Eat Brains, You are Safe: A Knowledge Infusion based Multitasking System for Sarcasm Detection in Meme—0
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks—0
A Contextual Word Embedding for Arabic Sarcasm Detection with Random Forests—0
A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict—0
akaBERT at SemEval-2022 Task 6: An Ensemble Transformer-based Model for Arabic Sarcasm Detection—0
All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on Memes—0
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis—0
Amrita_CEN at SemEval-2022 Task 6: A Machine Learning Approach for Detecting Intended Sarcasm using Oversampling—0
AMuSeD: An Attentive Deep Neural Network for Multimodal Sarcasm Detection Incorporating Bi-modal Data Augmentation—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
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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