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 101–150 of 266 papers

TitleStatusHype
Modeling Intra and Inter-modality Incongruity for Multi-Modal Sarcasm Detection—0
Modelling Sarcasm in Twitter, a Novel Approach—0
Modelling Visual Semantics via Image Captioning to extract Enhanced Multi-Level Cross-Modal Semantic Incongruity Representation with Attention for Multimodal Sarcasm Detection—0
muBoost: An Effective Method for Solving Indic Multilingual Text Classification Problem—0
Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection—0
Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model—0
Multi-task Learning Using a Combination of Contextualised and Static Word Embeddings for Arabic Sarcasm Detection and Sentiment Analysis—0
Multi-View Incongruity Learning for Multimodal Sarcasm Detection—0
Nek Minit: Harnessing Pragmatic Metacognitive Prompting for Explainable Sarcasm Detection of Australian and Indian English—0
Neural Sarcasm Detection using Conversation Context—0
NLPRL-IITBHU at SemEval-2018 Task 3: Combining Linguistic Features and Emoji pre-trained CNN for Irony Detection in Tweets—0
NULL at SemEval-2022 Task 6: Intended Sarcasm Detection Using Stylistically Fused Contextualized Representation and Deep Learning—0
On Prompt Sensitivity of ChatGPT in Affective Computing—0
On Sarcasm Detection with OpenAI GPT-based Models—0
PALI-NLP at SemEval-2022 Task 6: iSarcasmEval- Fine-tuning the Pre-trained Model for Detecting Intended Sarcasm—0
Parallel Deep Learning-Driven Sarcasm Detection from Pop Culture Text and English Humor Literature—0
Plumeria at SemEval-2022 Task 6: Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation—0
Polarity based Sarcasm Detection using Semigraph—0
Pragmatic Metacognitive Prompting Improves LLM Performance on Sarcasm Detection—0
Putting Sarcasm Detection into Context: The Effects of Class Imbalance and Manual Labelling on Supervised Machine Classification of Twitter Conversations—0
Quantifying sentence complexity based on eye-tracking measures—0
!Qué maravilla! Multimodal Sarcasm Detection in Spanish: a Dataset and a Baseline—0
¡Qué maravilla! Multimodal Sarcasm Detection in Spanish: a Dataset and a Baseline—0
R2D2 at SemEval-2022 Task 6: Are language models sarcastic enough? Finetuning pre-trained language models to identify sarcasm—0
Random Decision Syntax Trees at SemEval-2018 Task 3: LSTMs and Sentiment Scores for Irony Detection—0
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