natural-language-processing-3

Humor Detection

3260 papers • 126 benchmarks • 313 datasets

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

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in humor-detection-3

Trend

Dataset

Best Model

Actions

200k Short Texts for Humor Detection

Libraries

i

Use these libraries to find humor-detection-3 models and implementations

huggingface/transformers

2 papers 124,147

Datasets

UR-FUNNY

PHINC

Cards Against Humanity

Multimodal Humor Dataset

Subtasks

No subtasks available.

Most implemented papers

XGBoost: A Scalable Tree Boosting System

Tianqi Chen, Carlos Guestrin•Tue Mar 08 2016

This paper proposes a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning and provides insights on cache access patterns, data compression and sharding to build a scalable tree boosting system called XGBoost.

48813

Content

0

Paper Graph

ColBERT: Using BERT sentence embedding in parallel neural networks for computational humor

Issa Annamoradnejad, Gohar Zoghi•Sun Apr 26 2020

The evaluation performed on two contrasting settings confirm the strength and robustness of the model and suggests two important factors in achieving high accuracy in the current task: usage of sentence embeddings and utilizing the linguistic structure of humor in designing the proposed model.

36 0

Paper Graph

XLNet: Generalized Autoregressive Pretraining for Language Understanding

Quoc V. Le, Yiming Yang, R. Salakhutdinov, Zhilin Yang, Zihang Dai, J. Carbonell•Tue Jun 18 2019

XLNet is proposed, a generalized autoregressive pretraining method that enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and overcomes the limitations of BERT thanks to its autore progressive formulation.

9150 0

Paper Graph

MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis

Devamanyu Hazarika, Soujanya Poria, Roger Zimmermann•Wed May 06 2020

A novel framework, MISA, is proposed, which projects each modality to two distinct subspaces, which provide a holistic view of the multimodal data, which is used for fusion that leads to task predictions.

996 0

Paper Graph

Humor Detection: A Transformer Gets the Last Laugh

Orion Weller, Kevin Seppi•Fri Aug 30 2019

This paper builds a model that learns to identify humorous jokes based on ratings gleaned from Reddit pages, consisting of almost 16,000 labeled instances, and employs a Transformer architecture for its advantages in learning from sentence context.

140 0

Paper Graph

A Crowd-Annotated Spanish Corpus for Humor Analysis

Santiago Castro, Matías Cubero, Diego Garat, Guillermo Moncecchi•Sun Oct 01 2017

A corpus of 27,000 tweets written in Spanish and crowd-annotated by their humor value and funniness score, with about four annotations per tweet, tagged by 1,300 people over the Internet is presented.

47 0

Paper Graph

Is This a Joke? Detecting Humor in Spanish Tweets

Santiago Castro, Matías Cubero, Diego Garat, Guillermo Moncecchi•Tue Nov 22 2016

A crowdsourced corpus of labeled tweets is built, annotated according to its humor value, letting the annotators subjectively decide which are humorous.

34 0

Paper Graph

On the Use of Emojis to Train Emotion Classifiers

Mahmoud Al-Ayyoub, Wegdan A. Hussien, Yahya M. Tashtoush, M. Al-Kabi•Sat Feb 23 2019

This study proposes a distant supervised learning approach where the training sentences are automatically annotated based on the emojis they have, and experimentally shows that training classifier 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.

11 0

Paper Graph

Reverse-Engineering Satire, or "Paper on Computational Humor Accepted Despite Making Serious Advances"

E. Horvitz, Robert West•Wed Jan 09 2019

Overall, this paper deepens the understanding of the syntactic and semantic structure of satirical news headlines and provides insights for building humor-producing systems.

39 0

Paper Graph

DuluthNLP at SemEval-2021 Task 7: Fine-Tuning RoBERTa Model for Humor Detection and Offense Rating

Samuel Akrah•Thu Dec 31 2020

It is shown that optimizing hyper-parameters for learning rate, batch size and number of epochs can increase the accuracy and F1 score for humor detection.

4 0

Paper Graph

Adding a benchmark result helps the community track progress.