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 110 of 266 papers

TitleStatusHype
CAF-I: A Collaborative Multi-Agent Framework for Enhanced Irony Detection with Large Language Models0
Leveraging Large Language Models for Sarcastic Speech Annotation in Sarcasm Detection0
IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm DetectionCode0
Nek Minit: Harnessing Pragmatic Metacognitive Prompting for Explainable Sarcasm Detection of Australian and Indian English0
Token-free Models for Sarcasm Detection0
Assessing how hyperparameters impact Large Language Models' sarcasm detection performance0
Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models0
Sarcasm Detection as a Catalyst: Improving Stance Detection with Cross-Target Capabilities0
Intermediate-Task Transfer Learning: Leveraging Sarcasm Detection for Stance Detection0
Dual-level Adaptive Incongruity-enhanced Model for Multimodal Sarcasm DetectionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Bag-of-BigramsAccuracy76.5Unverified
2CASCADEAccuracy74Unverified