SOTAVerified

Humor Detection

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

Papers

Showing 5164 of 64 papers

TitleStatusHype
DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison0
Deceptive Humor: A Synthetic Multilingual Benchmark Dataset for Bridging Fabricated Claims with Humorous Content0
Deep Learning Techniques for Humor Detection in Hindi-English Code-Mixed Tweets0
DLJUST at SemEval-2021 Task 7: Hahackathon: Linking Humor and Offense0
Don't Take it Personally: Analyzing Gender and Age Differences in Ratings of Online Humor0
Duluth at SemEval-2017 Task 6: Language Models in Humor Detection0
Dutch Humor Detection by Generating Negative Examples0
EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and Ensembles0
From Generalized Laughter to Personalized Chuckles: Unleashing the Power of Data Fusion in Subjective Humor Detection0
Funny3 at SemEval-2020 Task 7: Humor Detection of Edited Headlines with LSTM and TFIDF Neural Network System0
Grenzlinie at SemEval-2021 Task 7: Detecting and Rating Humor and Offense0
#HashtagWars: Learning a Sense of Humor0
hub at SemEval-2021 Task 7: Fusion of ALBERT and Word Frequency Information Detecting and Rating Humor and Offense0
Humor as Circuits in Semantic Networks0
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Benchmark Results

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