Neural network based system used deep neural network model to generate short humorous short text. This category of model started with Yang and Sheng (2017). They used LSTM RNN as there neural network model, and trained the model on a dataset of 7,699 jokes written by Conan O’Brien and news data. 

University of Notre Dame

Humor generation can be viewed as a special case of automatic text generation, and current humor generation systems can be categorized to two major approaches:

1.	Neural network based systems

2.	Template-based systems


Types Of Humor Generation Systems

Amin, M., & Burghardt, M. (2020, December). A survey on approaches to computational humor generation. In Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (pp. 29-41).

A Survey on Approaches to Computational Humor Generation

Neural Network Based Systems

In linguistic, a template is a piece of text with slots that can be filled with different variables. To compose a joke, a template need to be associated with a schema, which stipulate the relationships between variables. Schema is typically chosen to provoke incongruity and its resolution. There are severals ways of selecting variables:

1.	Ontologies for variable selection

2.	Quantitative measures for variable selection

3.	Hybrid systems


Learn Before

Related