  World building forms the foundation of any task that requires narrative
intelligence. In this work, we focus on procedurally generating interactive
fiction worlds---text-based worlds that players "see" and "talk to" using
natural language. Generating these worlds requires referencing everyday and
thematic commonsense priors in addition to being semantically consistent,
interesting, and coherent throughout. Using existing story plots as
inspiration, we present a method that first extracts a partial knowledge graph
encoding basic information regarding world structure such as locations and
objects. This knowledge graph is then automatically completed utilizing
thematic knowledge and used to guide a neural language generation model that
fleshes out the rest of the world. We perform human participant-based
evaluations, testing our neural model's ability to extract and fill-in a
knowledge graph and to generate language conditioned on it against rule-based
and human-made baselines. Our code is available at
https://github.com/rajammanabrolu/WorldGeneration.
