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
Comment-aided Video-Language Alignment via Contrastive Pre-training for Short-form Video Humor Detection
yliu-cs/cvla
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The growing importance of multi-modal humor detection within affective computing correlates with the expanding influence of short-form video sharing on social media platforms.
Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysis
In this paper, we present our solutions for the Multimodal Sentiment Analysis Challenge (MuSe) 2022, which includes MuSe-Humor, MuSe-Reaction and MuSe-Stress Sub-challenges.
The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress
eihw/muse2022
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For this year's challenge, we feature three datasets: (i) the Passau Spontaneous Football Coach Humor (Passau-SFCH) dataset that contains audio-visual recordings of German football coaches, labelled for the presence of humour; (ii) the Hume-Reaction dataset in which reactions of individuals to emotional stimuli have been annotated with respect to seven emotional expression intensities, and (iii) the Ulm-Trier Social Stress Test (Ulm-TSST) dataset comprising of audio-visual data labelled with continuous emotion values (arousal and valence) of people in stressful dispositions.
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.
MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis
declare-lab/multimodal-deep-learning
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In this paper, we aim to learn effective modality representations to aid the process of fusion.
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.
XLNet: Generalized Autoregressive Pretraining for Language Understanding
huggingface/transformers
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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.
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.
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.
