Papers Sarcasm Detection
“Sarcasm Detection” 태그가 달린 논문 309편 · 필터 해제
CLASH: Counterfactual Auditing of Lexical and Prosodic Reliance in Spoken Sarcasm Detection
Spoken sarcasm detectors may exploit lexical content, prosody, or their interaction, yet conventional evaluation cannot reveal which cues drive their predictions. We introduce CLASH (Controlled Lexical-Acoustic Separatio…
Self-Supervised LearningSarcasm DetectionWhen Models Hear What They Expect: Diagnosing Prosodic Heuristics in Multimodal Sarcasm Detection
Multimodal Large Language Models (MLLMs) process speech and text jointly, yet whether they exploit prosodic cues for pragmatic inference or rely on surface acoustic patterns has received little systematic investigation. …
Sarcasm DetectionDynamic Gated Cross-Modal Fusion with Sarcastic-aware Contrastive Regularization for Multimodal Sarcasm Detection
Multimodal sarcasm detection aims to identify sarcastic intent from multimodal content, where inconsistencies between literal meaning and contextual cues often signal irony. This task has attracted increasing research at…
Sarcasm DetectionProCrit: Self-Elicited Multi-Perspective Reasoning with Critic-Guided Revision for Multimodal Sarcasm Detection
Multimodal sarcasm detection requires reasoning over cross-modal incongruities between literal expression and intended meaning, yet the specific analytical perspectives needed vary across samples due to the diversity of …
Reinforcement LearningSarcasm DetectionMultiSoc-4D: A Benchmark for Diagnosing Instruction-Induced Label Collapse in Closed-Set LLM Annotation of Bengali Social Media
Annotation automation via Large Language Models (LLMs) is the core approach for scaling NLP datasets; however, LLM behavior with respect to closed-set instructions in low-resource languages has not been well studied. We …
Sarcasm DetectionEnhancing Multimodal In-Context Learning via Inductive-Deductive Reasoning
In-context learning (ICL) allows large models to adapt to tasks using a few examples, yet its extension to vision-language models (VLMs) remains fragile. Our analysis reveals that the fundamental limitation lies in an in…
Reinforcement LearningSarcasm DetectionLogical ReasoningPC-MNet: Dual-Level Congruity Modeling for Multimodal Sarcasm Detection via Polarity-Modulated Attention
Multimodal sarcasm detection, which aims to precisely identify pragmatic incongruities between literal text and nonverbal cues, has gained substantial attention in multimodal understanding. Recent advancements have predo…
Contrastive LearningSarcasm DetectionParameter-efficient Quantum Multi-task Learning
Multi-task learning (MTL) improves generalization and data efficiency by jointly learning related tasks through shared representations. In the widely used hard-parameter-sharing setting, a shared backbone is combined wit…
Quantum Machine LearningMulti-Task LearningSarcasm DetectionGRASP: Grounded CoT Reasoning with Dual-Stage Optimization for Multimodal Sarcasm Target Identification
Moving beyond the traditional binary classification paradigm of Multimodal Sarcasm Detection, Multimodal Sarcasm Target Identification (MSTI) presents a more formidable challenge, requiring precise localization of fine-g…
Binary ClassificationSarcasm DetectionVisual GroundingA GAN and LLM-Driven Data Augmentation Framework for Dynamic Linguistic Pattern Modeling in Chinese Sarcasm Detection
Sarcasm is a rhetorical device that expresses criticism or emphasizes characteristics of certain individuals or situations through exaggeration, irony, or comparison. Existing methods for Chinese sarcasm detection are co…
Sarcasm DetectionData AugmentationURMF: Uncertainty-aware Robust Multimodal Fusion for Multimodal Sarcasm Detection
Multimodal sarcasm detection (MSD) aims to identify sarcastic intent from semantic incongruity between text and image. Although recent methods have improved MSD through cross-modal interaction and incongruity reasoning, …
Contrastive LearningSarcasm DetectionCFMS: Towards Explainable and Fine-Grained Chinese Multimodal Sarcasm Detection Benchmark
Multimodal sarcasm detection has recently garnered significant attention. However, existing benchmarks suffer from coarse-grained annotations and limited cultural coverage, which hinder research into fine-grained semanti…
Explanation GenerationReinforcement LearningSarcasm DetectionSarcasmMiner: A Dual-Track Post-Training Framework for Robust Audio-Visual Sarcasm Reasoning
Multimodal sarcasm detection requires resolving pragmatic incongruity across textual, acoustic, and visual cues through cross-modal reasoning. To enable robust sarcasm reasoning with foundation models, we propose Sarcasm…
Reinforcement LearningMultimodal ReasoningSarcasm DetectionSmall Wins Big: Comparing Large Language Models and Domain Fine-Tuned Models for Sarcasm Detection in Code-Mixed Hinglish Text
Sarcasm detection in multilingual and code-mixed environments remains a challenging task for natural language processing models due to structural variations, informal expressions, and low-resource linguistic availability…
Sarcasm DetectionYor-Sarc: A gold-standard dataset for sarcasm detection in a low-resource African language
Sarcasm detection poses a fundamental challenge in computational semantics, requiring models to resolve disparities between literal and intended meaning. The challenge is amplified in low-resource languages where annotat…
Sarcasm DetectionMuVaC: A Variational Causal Framework for Multimodal Sarcasm Understanding in Dialogues
The prevalence of sarcasm in multimodal dialogues on the social platforms presents a crucial yet challenging task for understanding the true intent behind online content. Comprehensive sarcasm analysis requires two key a…
Explanation GenerationSarcasm DetectionCausal InferenceGDCNet: Generative Discrepancy Comparison Network for Multimodal Sarcasm Detection
Multimodal sarcasm detection (MSD) aims to identify sarcasm within image-text pairs by modeling semantic incongruities across modalities. Existing methods often exploit cross-modal embedding misalignment to detect incons…
Sarcasm DetectionRAM-SD: Retrieval-Augmented Multi-agent framework for Sarcasm Detection
Sarcasm detection remains a significant challenge due to its reliance on nuanced contextual understanding, world knowledge, and multi-faceted linguistic cues that vary substantially across different sarcastic expressions…
Sarcasm DetectionGRCF: Two-Stage Groupwise Ranking and Calibration Framework for Multimodal Sentiment Analysis
Most Multimodal Sentiment Analysis research has focused on point-wise regression. While straightforward, this approach is sensitive to label noise and neglects whether one sample is more positive than another, resulting …
Multimodal Sentiment AnalysisSarcasm DetectionHumor DetectionWorld model inspired sarcasm reasoning with large language model agents
Sarcasm understanding is a challenging problem in natural language processing, as it requires capturing the discrepancy between the surface meaning of an utterance and the speaker's intentions as well as the surrounding …
Sarcasm Detection