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
Most implemented papers
XGBoost: A Scalable Tree Boosting System
In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges.
XLNet: Generalized Autoregressive Pretraining for Language Understanding
zihangdai/xlnet
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NeurIPS 2019
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
ColBERT: Using BERT Sentence Embedding in Parallel Neural Networks for Computational Humor
The proposed technical method initiates by separating sentences of the given text and utilizing the BERT model to generate embeddings for each one.
Humor Detection: A Transformer Gets the Last Laugh
orionw/RedditHumorDetection
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IJCNLP 2019
These experiments show that this method outperforms all previous work done on these tasks, with an F-measure of 93. 1% for the Puns dataset and 98. 6% on the Short Jokes dataset.
MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis
declare-lab/MISA
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In this paper, we aim to learn effective modality representations to aid the process of fusion.
Is This a Joke? Detecting Humor in Spanish Tweets
While humor has been historically studied from a psychological, cognitive and linguistic standpoint, its study from a computational perspective is an area yet to be explored in Computational Linguistics.
A Crowd-Annotated Spanish Corpus for Humor Analysis
Computational Humor involves several tasks, such as humor recognition, humor generation, and humor scoring, for which it is useful to have human-curated data.
Reverse-Engineering Satire, or "Paper on Computational Humor Accepted Despite Making Serious Advances"
Starting from the observation that satirical news headlines tend to resemble serious news headlines, we build and analyze a corpus of satirical headlines paired with nearly identical but serious headlines.
On the Use of Emojis to Train Emotion Classifiers
Nonetheless, we experimentally show that training classifiers on cheap, large and possibly erroneous data annotated using this approach leads to more accurate results compared with training the same classifiers on the more expensive, much smaller and error-free manually annotated training data.
DuluthNLP at SemEval-2021 Task 7: Fine-Tuning RoBERTa Model for Humor Detection and Offense Rating
This paper presents the DuluthNLP submission to Task 7 of the SemEval 2021 competition on Detecting and Rating Humor and Offense.
