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
Enhancing Multimodal Affective Analysis with Learned Live Comment Features—0
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
Impact of emoji exclusion on the performance of Arabic sarcasm detection models—0
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
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
A Wide Evaluation of ChatGPT on Affective Computing TasksCode0
Sarcasm Detection in a Disaster ContextCode0
A big data approach towards sarcasm detection in RussianCode0
Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science—0
Researchers eye-view of sarcasm detection in social media textual content—0
Thematic context vector association based on event uncertainty for Twitter—0
Polarity based Sarcasm Detection using Semigraph—0
BloombergGPT: A Large Language Model for FinanceCode0
Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal ClassificationCode0
TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models—0
Interpretable Bangla Sarcasm Detection using BERT and Explainable AI—0
TEDB System Description to a Shared Task on Euphemism Detection 2022Code0
SAIDS: A Novel Approach for Sentiment Analysis Informed of Dialect and Sarcasm—0
Finetuning for Sarcasm Detection with a Pruned DatasetCode0
X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detection—0
Sarcasm Detection Framework Using Context, Emotion and Sentiment Features—0
Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal DialoguesCode0
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis—0
面向话题的讽刺识别:新任务、新数据和新方法(Topic-Oriented Sarcasm Detection: New Task, New Dataset and New Method)—0
Emotion Enriched Retrofitted Word Embeddings—0
BanglaSarc: A Dataset for Sarcasm Detection—0
Computational Sarcasm Analysis on Social Media: A Systematic Review—0
Amrita_CEN at SemEval-2022 Task 6: A Machine Learning Approach for Detecting Intended Sarcasm using Oversampling—0
R2D2 at SemEval-2022 Task 6: Are language models sarcastic enough? Finetuning pre-trained language models to identify sarcasm—0
connotation_clashers at SemEval-2022 Task 6: The effect of sentiment analysis on sarcasm detection—0
PALI-NLP at SemEval-2022 Task 6: iSarcasmEval- Fine-tuning the Pre-trained Model for Detecting Intended Sarcasm—0
ISD at SemEval-2022 Task 6: Sarcasm Detection Using Lightweight Models—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