Toward Automated Story Generation with Markov Chain Monte Carlo Methods and Deep Neural Networks

Harrison, Brent (Georgia Institute of Technology) | Purdy, Christopher (Georgia Institute of Technology) | Riedl, Mark O. (Georgia Institute of Technology)

AAAI Conferences 

In this paper, we introduce an approach to automated story generation using Markov Chain Monte Carlo (MCMC) sampling. This approach uses a sampling algorithm based on Metropolis-Hastings to generate a probability distribution which can be used to generate stories via random sampling that adhere to criteria learned by recurrent neural networks. We show the applicability of our technique through a case study where we generate novel stories using an acceptance criteria learned from a set of movie plots taken from Wikipedia. This study shows that stories generated using this approach adhere to this criteria 85%-86% of the time.