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 126–150 of 266 papers

TitleStatusHype
Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic Association—0
Reasoning with Sarcasm by Reading In-between—0
Researchers eye-view of sarcasm detection in social media textual content—0
Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning—0
Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection—0
SAIDS: A Novel Approach for Sentiment Analysis Informed of Dialect and Sarcasm—0
Sarcasm Analysis using Conversation Context—0
Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation—0
Sarcasm and Sentiment Detection in Arabic: investigating the interest of character-level features—0
SarcasmDet at Sarcasm Detection Task 2021 in Arabic using AraBERT Pretrained Model—0
SarcasmDet at SemEval-2022 Task 6: Detecting Sarcasm using Pre-trained Transformers in English and Arabic Languages—0
Sarcasm Detection: A Comparative Study—0
Sarcasm Detection and Building an English Language Corpus in Real Time—0
sarcasm detection and quantification in arabic tweets—0
Sarcasm Detection as a Catalyst: Improving Stance Detection with Cross-Target Capabilities—0
Sarcasm Detection : Building a Contextual Hierarchy—0
Sarcasm Detection Framework Using Context, Emotion and Sentiment Features—0
Sarcasm Detection in Chinese Using a Crowdsourced Corpus—0
Sarcasm Detection in Tweets with BERT and GloVe Embeddings—0
#SarcasmDetection is soooo general! Towards a Domain-Independent Approach for Detecting Sarcasm—0
Sarcasm Detection on Czech and English Twitter—0
Sarcasm Detection Using an Ensemble Approach—0
Sarcasm Detection using Context Separators in Online Discourse—0
Sarcasm Identification and Detection in Conversion Context using BERT—0
Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection—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