Joke generation using Deep Neural Networks

In the last few decades, the development of informatics and information technology has brought new exploration opportunities. In particular, the development of artificial intelligence and achievements in the use of neural network models have been a major step. One area of artificial intelligence is the generation of text that includes a wide range of possible tasks, from generating reviews, stories, poems, song lyrics to generating article titles, or generating jokes. It is the computational humor that deals with generation of jokes. Although direct use may not be obvious, the main goal of this area is to make communication between devices and people more enjoyable.

In recent years, a large number of devices, such as smart mobile phones, laptops, or other devices containing intelligent assistants have brought into people’s life. The interaction with such non-human assistants is still very unnatural for ordinary people. With humor, which is considered as an ice-breaker in normal interpersonal communication, the use of humor in intelligent assistants could give the impression of more-human communication.

The goal of my paper is analyzing the current state of computational humor, including datasets and applied approaches or methods and also to design or extend an existing approach. In later stages, the implementation itself and the verification of the success of generating meaningful jokes.