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
Transit Pulse: Utilizing Social Media as a Source for Customer Feedback and Information Extraction with Large Language Model—0
Sarcasm Detection in a Less-Resourced LanguageCode0
SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding—0
Modelling Visual Semantics via Image Captioning to extract Enhanced Multi-Level Cross-Modal Semantic Incongruity Representation with Attention for Multimodal Sarcasm Detection—0
Is Sarcasm Detection A Step-by-Step Reasoning Process in Large Language Models?—0
InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm DetectionCode1
Impact of emoji exclusion on the performance of Arabic sarcasm detection models—0
CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal ModelsCode1
Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!Code0
On Prompt Sensitivity of ChatGPT in Affective Computing—0
Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding—0
Multi-modal Semantic Understanding with Contrastive Cross-modal Feature AlignmentCode0
MIKO: Multimodal Intention Knowledge Distillation from Large Language Models for Social-Media Commonsense Discovery—0
InfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks—0
KoCoSa: Korean Context-aware Sarcasm Detection DatasetCode0
Systematic Literature Review: Computational Approaches for Humour Style Classification—0
DocMSU: A Comprehensive Benchmark for Document-level Multimodal Sarcasm UnderstandingCode0
Debiasing Multimodal Sarcasm Detection with Contrastive Learning—0
On Sarcasm Detection with OpenAI GPT-based Models—0
MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction ExpertsCode1
Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning—0
An Evaluation of State-of-the-Art Large Language Models for Sarcasm Detection—0
Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection—0
Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary TasksCode0
BNS-Net: A Dual-channel Sarcasm Detection Method Considering Behavior-level and Sentence-level Conflicts—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