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 1–50 of 266 papers

TitleStatusHype
Training Compute-Optimal Large Language ModelsCode6
Scaling Language Models: Methods, Analysis & Insights from Training GopherCode2
Dual-level Adaptive Incongruity-enhanced Model for Multimodal Sarcasm DetectionCode1
RCLMuFN: Relational Context Learning and Multiplex Fusion Network for Multimodal Sarcasm DetectionCode1
InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm DetectionCode1
CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal ModelsCode1
MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction ExpertsCode1
MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection SystemCode1
DIP: Dual Incongruity Perceiving Network for Sarcasm DetectionCode1
Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge EnhancementCode1
News Headlines Dataset For Sarcasm DetectionCode1
Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text ClassificationCode1
SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and ArabicCode1
A Multimodal Corpus for Emotion Recognition in SarcasmCode1
Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional NetworkCode1
UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection Using Generative-based and Mutation-based Data AugmentationCode1
When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party DialoguesCode1
Perceived and Intended Sarcasm Detection with Graph Attention NetworksCode1
Multi-Modal Sarcasm Detection Based on Contrastive Attention MechanismCode1
Multi-modal Sarcasm Detection and Humor Classification in Code-mixed ConversationsCode1
Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in ArabicCode1
Affective and Contextual Embedding for Sarcasm DetectionCode1
“Did you really mean what you said?” : Sarcasm Detection in Hindi-English Code-Mixed Data using Bilingual Word EmbeddingsCode1
"Did you really mean what you said?" : Sarcasm Detection in Hindi-English Code-Mixed Data using Bilingual Word EmbeddingsCode1
Reactive Supervision: A New Method for Collecting Sarcasm DataCode1
Sarcasm Detection using Hybrid Neural NetworkCode1
The Role of Conversation Context for Sarcasm Detection in Online InteractionsCode1
Modelling Context with User Embeddings for Sarcasm Detection in Social MediaCode1
CAF-I: A Collaborative Multi-Agent Framework for Enhanced Irony Detection with Large Language Models—0
Leveraging Large Language Models for Sarcastic Speech Annotation in Sarcasm Detection—0
IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm DetectionCode0
Nek Minit: Harnessing Pragmatic Metacognitive Prompting for Explainable Sarcasm Detection of Australian and Indian English—0
Token-free Models for Sarcasm Detection—0
Assessing how hyperparameters impact Large Language Models' sarcasm detection performance—0
Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models—0
Intermediate-Task Transfer Learning: Leveraging Sarcasm Detection for Stance Detection—0
Sarcasm Detection as a Catalyst: Improving Stance Detection with Cross-Target Capabilities—0
Evaluating Large Language Models Against Human Annotators in Latent Content Analysis: Sentiment, Political Leaning, Emotional Intensity, and Sarcasm—0
Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection—0
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning—0
AMuSeD: An Attentive Deep Neural Network for Multimodal Sarcasm Detection Incorporating Bi-modal Data Augmentation—0
BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English—0
Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks—0
Pragmatic Metacognitive Prompting Improves LLM Performance on Sarcasm Detection—0
Multi-View Incongruity Learning for Multimodal Sarcasm Detection—0
Was that Sarcasm?: A Literature Survey on Sarcasm Detection—0
Gender Bias Mitigation for Bangla Classification TasksCode0
YouTube Comments Decoded: Leveraging LLMs for Low Resource Language Classification—0
An Innovative CGL-MHA Model for Sarcasm Sentiment Recognition Using the MindSpore FrameworkCode0
A Survey of 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