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

Showing 2130 of 266 papers

TitleStatusHype
Modelling Context with User Embeddings for Sarcasm Detection in Social MediaCode1
Multi-modal Sarcasm Detection and Humor Classification in Code-mixed ConversationsCode1
Dual-level Adaptive Incongruity-enhanced Model for Multimodal Sarcasm DetectionCode1
News Headlines Dataset For Sarcasm DetectionCode1
RCLMuFN: Relational Context Learning and Multiplex Fusion Network for Multimodal Sarcasm DetectionCode1
Reactive Supervision: A New Method for Collecting Sarcasm DataCode1
SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and ArabicCode1
The Role of Conversation Context for Sarcasm Detection in Online InteractionsCode1
A Survey of Multimodal Sarcasm Detection0
Amrita_CEN at SemEval-2022 Task 6: A Machine Learning Approach for Detecting Intended Sarcasm using Oversampling0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PaLM 2(few-shot, k=3, CoT)Accuracy84.8Unverified
2PaLM 2 (few-shot, k=3, Direct)Accuracy78.7Unverified
3PaLM 540B (few-shot, k=3)Accuracy78.1Unverified
4BLOOM 176B (few-shot, k=3)Accuracy72.47Unverified
5Bloomberg GPT (few-shot, k=3)Accuracy69.66Unverified
6GPT-NeoX (few-shot, k=3)Accuracy62.36Unverified
7Chinchilla-70B (few-shot, k=5)Accuracy58.6Unverified
8Gopher-280B (few-shot, k=5)Accuracy48.3Unverified
#ModelMetricClaimedVerifiedStatus
1BERT+Aspect-based approachesF10.74Unverified
2RoBERTa_large - (Separated Context-Response)F10.72Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa_large (Context-Response)F10.77Unverified
2BERTF10.73Unverified
#ModelMetricClaimedVerifiedStatus
1CASCADEAccuracy77Unverified
2Bag-of-BigramsAccuracy75.8Unverified
#ModelMetricClaimedVerifiedStatus
1Bag-of-BigramsAccuracy76.5Unverified
2CASCADEAccuracy74Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa + Mutation Data AugmentationF1-Score0.41Unverified
#ModelMetricClaimedVerifiedStatus
1MUStARD++Precision70.2Unverified
#ModelMetricClaimedVerifiedStatus
1Bag-of-WordsAvg F127Unverified
#ModelMetricClaimedVerifiedStatus
1BARTR136.88Unverified