Humor Detection
14 papers with code • 1 benchmarks • 4 datasets
Humor detection is the task of identifying comical or amusing elements.
Benchmarks
These leaderboards are used to track progress in Humor Detection
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Libraries
Use these libraries to find Humor Detection models and implementations
UR-FUNNY
Cards Against Humanity
Latest papers with no code
DLJUST at SemEval-2021 Task 7: Hahackathon: Linking Humor and Offense
Humor detection and rating poses interesting linguistic challenges to NLP; it is highly subjective depending on the perceptions of a joke and the context in which it is used.
CS-UM6P at SemEval-2021 Task 7: Deep Multi-Task Learning Model for Detecting and Rating Humor and Offense
Humor detection has become a topic of interest for several research teams, especially those involved in socio-psychological studies, with the aim to detect the humor and the temper of a targeted population (e. g. a community, a city, a country, the employees of a given company).
hub at SemEval-2021 Task 7: Fusion of ALBERT and Word Frequency Information Detecting and Rating Humor and Offense
The final scores of the prediction results of the two subtask test sets submitted by our team are task1a 0. 921 (F1), task1a 0. 9364 (Accuracy), task1b 0. 6288 (RMSE), task1c 0. 5333 (F1), task1c 0. 0. 5591 (Accuracy), and task2 0. 5027 (RMSE) respectively.
UoR at SemEval-2021 Task 7: Utilizing Pre-trained DistilBERT Model and Multi-scale CNN for Humor Detection
Humour detection is an interesting but difficult task in NLP.
RedwoodNLP at SemEval-2021 Task 7: Ensembled Pretrained and Lightweight Models for Humor Detection
An understanding of humor is an essential component of human-facing NLP systems.
EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and Ensembles
This paper describes Humor-BERT, a set of BERT Large based models that we used in the SemEval-2021 Task 7: Detecting and Rating Humor and Offense.
Funny3 at SemEval-2020 Task 7: Humor Detection of Edited Headlines with LSTM and TFIDF Neural Network System
This paper presents a neural network system where we participate in the first task of SemEval-2020 shared task 7 {``}Assessing the Funniness of Edited News Headlines{''}.
MLEngineer at SemEval-2020 Task 7: BERT-Flair Based Humor Detection Model (BFHumor)
Task 7, Assessing the Funniness of Edited News Headlines, in the International Workshop SemEval2020 introduces two sub-tasks to predict the funniness values of edited news headlines from the Reddit website.
UniTuebingenCL at SemEval-2020 Task 7: Humor Detection in News Headlines
This paper describes the work done by the team UniTuebingenCL for the SemEval 2020 Task 7: {``}Assessing the Funniness of Edited News Headlines{''}.
Dutch Humor Detection by Generating Negative Examples
Detecting if a text is humorous is a hard task to do computationally, as it usually requires linguistic and common sense insights.
