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 226250 of 266 papers

TitleStatusHype
AlexU-AL at SemEval-2022 Task 6: Detecting Sarcasm in Arabic Text Using Deep Learning TechniquesCode0
Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)Code0
A Wide Evaluation of ChatGPT on Affective Computing TasksCode0
How Effective is Incongruity? Implications for Code-mix Sarcasm DetectionCode0
How effective is incongruity? Implications for code-mixed sarcasm detectionCode0
A Transformer-based approach to Irony and Sarcasm detectionCode0
Does Commonsense help in detecting Sarcasm?Code0
DocMSU: A Comprehensive Benchmark for Document-level Multimodal Sarcasm UnderstandingCode0
reamtchka at SemEval-2022 Task 6: Investigating the effect of different loss functions for Sarcasm detection for unbalanced datasetsCode0
Deep and Dense Sarcasm DetectionCode0
Multi-modal Semantic Understanding with Contrastive Cross-modal Feature AlignmentCode0
Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary TasksCode0
Sarcasm Detection in a Disaster ContextCode0
Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource LanguageCode0
Finetuning for Sarcasm Detection with a Pruned DatasetCode0
Sarcasm Detection in a Less-Resourced LanguageCode0
Representing Social Media Users for Sarcasm DetectionCode0
Towards Multimodal Sarcasm Detection (An \_Obviously\_ Perfect Paper)Code0
IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm DetectionCode0
CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and ArabicCode0
A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment ConflictCode0
Sarcasm Detection in Twitter -- Performance Impact while using Data Augmentation: Word EmbeddingsCode0
Robust Gram EmbeddingsCode0
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural NetworksCode0
Context-Dependent Sentiment Analysis in User-Generated VideosCode0
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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