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Sarcasm Detection
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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.
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Leveraging Large Language Models for Sarcastic Speech Annotation in Sarcasm Detection
IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detection
Nek Minit: Harnessing Pragmatic Metacognitive Prompting for Explainable Sarcasm Detection of Australian and Indian English
Token-free Models for Sarcasm Detection
Assessing how hyperparameters impact Large Language Models' sarcasm detection performance
Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models
Intermediate-Task Transfer Learning: Leveraging Sarcasm Detection for Stance Detection
Sarcasm Detection as a Catalyst: Improving Stance Detection with Cross-Target Capabilities
Dual-level Adaptive Incongruity-enhanced Model for Multimodal Sarcasm Detection
Evaluating Large Language Models Against Human Annotators in Latent Content Analysis: Sentiment, Political Leaning, Emotional Intensity, and Sarcasm
Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning
RCLMuFN: Relational Context Learning and Multiplex Fusion Network for Multimodal Sarcasm Detection
AMuSeD: An Attentive Deep Neural Network for Multimodal Sarcasm Detection Incorporating Bi-modal Data Augmentation
BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English
Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks
Pragmatic Metacognitive Prompting Improves LLM Performance on Sarcasm Detection
Multi-View Incongruity Learning for Multimodal Sarcasm Detection
Was that Sarcasm?: A Literature Survey on Sarcasm Detection
Gender Bias Mitigation for Bangla Classification Tasks
YouTube Comments Decoded: Leveraging LLMs for Low Resource Language Classification
An Innovative CGL-MHA Model for Sarcasm Sentiment Recognition Using the MindSpore Framework
A Survey of Multimodal Sarcasm Detection
Enhancing Multimodal Affective Analysis with Learned Live Comment Features
Transit Pulse: Utilizing Social Media as a Source for Customer Feedback and Information Extraction with Large Language Model
Sarcasm Detection in a Less-Resourced Language
SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding
Modelling Visual Semantics via Image Captioning to extract Enhanced Multi-Level Cross-Modal Semantic Incongruity Representation with Attention for Multimodal Sarcasm Detection
Is Sarcasm Detection A Step-by-Step Reasoning Process in Large Language Models?
InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detection
Impact of emoji exclusion on the performance of Arabic sarcasm detection models
CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models
Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!
On Prompt Sensitivity of ChatGPT in Affective Computing
Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding
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