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 26–50 of 266 papers

TitleStatusHype
Sarcasm Detection using Hybrid Neural NetworkCode1
The Role of Conversation Context for Sarcasm Detection in Online InteractionsCode1
Modelling Context with User Embeddings for Sarcasm Detection in Social MediaCode1
CAF-I: A Collaborative Multi-Agent Framework for Enhanced Irony Detection with Large Language Models—0
Leveraging Large Language Models for Sarcastic Speech Annotation in Sarcasm Detection—0
IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm DetectionCode0
Nek Minit: Harnessing Pragmatic Metacognitive Prompting for Explainable Sarcasm Detection of Australian and Indian English—0
Token-free Models for Sarcasm Detection—0
Assessing how hyperparameters impact Large Language Models' sarcasm detection performance—0
Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models—0
Intermediate-Task Transfer Learning: Leveraging Sarcasm Detection for Stance Detection—0
Sarcasm Detection as a Catalyst: Improving Stance Detection with Cross-Target Capabilities—0
Evaluating Large Language Models Against Human Annotators in Latent Content Analysis: Sentiment, Political Leaning, Emotional Intensity, and Sarcasm—0
Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection—0
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning—0
AMuSeD: An Attentive Deep Neural Network for Multimodal Sarcasm Detection Incorporating Bi-modal Data Augmentation—0
BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English—0
Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks—0
Pragmatic Metacognitive Prompting Improves LLM Performance on Sarcasm Detection—0
Multi-View Incongruity Learning for Multimodal Sarcasm Detection—0
Was that Sarcasm?: A Literature Survey on Sarcasm Detection—0
Gender Bias Mitigation for Bangla Classification TasksCode0
YouTube Comments Decoded: Leveraging LLMs for Low Resource Language Classification—0
An Innovative CGL-MHA Model for Sarcasm Sentiment Recognition Using the MindSpore FrameworkCode0
A Survey of Multimodal 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