SOTAVerified

Humor Detection

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 26–50 of 64 papers

TitleStatusHype
LOLgorithm: Integrating Semantic,Syntactic and Contextual Elements for Humor Classification—0
MLEngineer at SemEval-2020 Task 7: BERT-Flair Based Humor Detection Model (BFHumor)—0
Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection—0
QUB at SemEval-2017 Task 6: Cascaded Imbalanced Classification for Humor Analysis in Twitter—0
Reasoning in Conversation: Solving Subjective Tasks through Dialogue Simulation for Large Language Models—0
RedwoodNLP at SemEval-2021 Task 7: Ensembled Pretrained and Lightweight Models for Humor Detection—0
SemEval-2017 Task 6: \#HashtagWars: Learning a Sense of Humor—0
SemEval 2021 Task 7: HaHackathon, Detecting and Rating Humor and Offense—0
SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks—0
StandUp4AI: A New Multilingual Dataset for Humor Detection in Stand-up Comedy Videos—0
TakeLab at SemEval-2017 Task 6: \#RankingHumorIn4Pages—0
TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models—0
The Naughtyformer: A Transformer Understands Offensive Humor—0
Unified Humor Detection Based on Sentence-pair Augmentation and Transfer Learning—0
UniTuebingenCL at SemEval-2020 Task 7: Humor Detection in News Headlines—0
UoR at SemEval-2021 Task 7: Utilizing Pre-trained DistilBERT Model and Multi-scale CNN for Humor Detection—0
UR-FUNNY: A Multimodal Language Dataset for Understanding Humor—0
\#WarTeam at SemEval-2017 Task 6: Using Neural Networks for Discovering Humorous Tweets—0
What makes us laugh? Investigations into Automatic Humor Classification—0
THInC: A Theory-Driven Framework for Computational Humor Detection—0
A Sentiment and Emotion Aware Multimodal Multiparty Humor Recognition in Multilingual Conversational Setting—0
AVR: Synergizing Foundation Models for Audio-Visual Humor Detection—0
CHoRaL: Collecting Humor Reaction Labels from Millions of Social Media Users—0
Crossing the Line: Where do Demographic Variables Fit into Humor Detection?—0
CS-UM6P at SemEval-2021 Task 7: Deep Multi-Task Learning Model for Detecting and Rating Humor and Offense—0
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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