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 125 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
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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