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 1–25 of 266 papers

TitleStatusHype
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
Sarcasm Detection as a Catalyst: Improving Stance Detection with Cross-Target Capabilities—0
Intermediate-Task Transfer Learning: Leveraging Sarcasm Detection for Stance Detection—0
Dual-level Adaptive Incongruity-enhanced Model for Multimodal Sarcasm DetectionCode1
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
RCLMuFN: Relational Context Learning and Multiplex Fusion Network for Multimodal Sarcasm DetectionCode1
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
Pragmatic Metacognitive Prompting Improves LLM Performance on Sarcasm Detection—0
Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks—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
Enhancing Multimodal Affective Analysis with Learned Live Comment Features—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