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 51–75 of 266 papers

TitleStatusHype
A Wide Evaluation of ChatGPT on Affective Computing TasksCode0
Sarcasm Detection in a Disaster ContextCode0
MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection SystemCode1
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 Finance—0
TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models—0
Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal ClassificationCode0
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
DIP: Dual Incongruity Perceiving Network for Sarcasm DetectionCode1
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
Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge EnhancementCode1
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis—0
Emotion Enriched Retrofitted Word Embeddings—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
BanglaSarc: A Dataset for Sarcasm Detection—0
News Headlines Dataset For Sarcasm DetectionCode1
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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