SOTAVerified

Humor Detection

Humor detection is the task of identifying comical or amusing elements.

Papers

Showing 1–10 of 64 papers

TitleStatusHype
StandUp4AI: A New Multilingual Dataset for Humor Detection in Stand-up Comedy Videos—0
Deceptive Humor: A Synthetic Multilingual Benchmark Dataset for Bridging Fabricated Claims with Humorous Content—0
MemeCLIP: Leveraging CLIP Representations for Multimodal Meme ClassificationCode1
THInC: A Theory-Driven Framework for Computational Humor Detection—0
LOLgorithm: Integrating Semantic,Syntactic and Contextual Elements for Humor Classification—0
AVR: Synergizing Foundation Models for Audio-Visual Humor Detection—0
The MuSe 2024 Multimodal Sentiment Analysis Challenge: Social Perception and Humor RecognitionCode0
SynthesizRR: Generating Diverse Datasets with Retrieval AugmentationCode1
Reasoning in Conversation: Solving Subjective Tasks through Dialogue Simulation for Large Language Models—0
Getting Serious about Humor: Crafting Humor Datasets with Unfunny Large Language ModelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ColBERT modelF1-score0.98—Unverified
2XLNet Large CasedF1-score0.92—Unverified
3Multinomial NBF1-score0.88—Unverified
4SVMF1-score0.87—Unverified
5XGBoostF1-score0.81—Unverified
6Decision TreeF1-score0.79—Unverified