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 201–250 of 266 papers

TitleStatusHype
¡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
Finetuning for Sarcasm Detection with a Pruned DatasetCode0
TEDB System Description to a Shared Task on Euphemism Detection 2022Code0
Sarcasm Target Identification: Dataset and An Introductory ApproachCode0
Gender Bias Mitigation for Bangla Classification TasksCode0
Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!Code0
A big data approach towards sarcasm detection in RussianCode0
Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal DialoguesCode0
Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze BehaviourCode0
Effectiveness of Data-Driven Induction of Semantic Spaces and Traditional Classifiers for Sarcasm DetectionCode0
An Innovative CGL-MHA Model for Sarcasm Sentiment Recognition Using the MindSpore FrameworkCode0
Does Commonsense help in detecting Sarcasm?Code0
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
DocMSU: A Comprehensive Benchmark for Document-level Multimodal Sarcasm UnderstandingCode0
Deep and Dense Sarcasm DetectionCode0
reamtchka at SemEval-2022 Task 6: Investigating the effect of different loss functions for Sarcasm detection for unbalanced datasetsCode0
CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and ArabicCode0
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
FiLMing Multimodal Sarcasm Detection with AttentionCode0
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
Context-Dependent Sentiment Analysis in User-Generated VideosCode0
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
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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