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. 

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


Template-Based Systems

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


University of Notre Dame

Humor Generation is the task of creating jokes using algorithms, especially deep neural network models recently. However, until today, there is still no system that can generate jokes like human.

Humor Generation

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

Types Of Humor Generation Systems

To compare different joke generation systems, some basic quality measures are needed. However, until now a standard method of evaluating humor generation systems are still missing. Most approaches use humorousness of the output as an evaluation criterion, and let actual human rate the jokes. The main challenge of this method is that it’s highly subjective. As an alternative, Binsted and Ritchie (1997) suggest that to evaluate jokes based on whether people can tell it apart from a human-generated joke.

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