Sarcasm Detection
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Benchmarks
BIG-bench (SNARKS)
SARC (all-bal)
SARC (pol-bal)
MUStARD++
SARC (pol-unbal)
WITS
iSarcasm
Most implemented
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
A Large Self-Annotated Corpus for Sarcasm
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Sarcasm Detection using Hybrid Neural Network
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks
Modelling Context with User Embeddings for Sarcasm Detection in Social Media
Papers
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 Reasoning