Neural Story Planning

Automated plot generation is the challenge of generating a sequence of events that will be perceived by readers as the plot of a coherent story. Traditional symbolic planners plan a story from a goal state and guarantee logical causal plot coherence but rely on a library of hand-crafted actions with their preconditions and effects. This closed world setting limits the length and diversity of what symbolic planners can generate. On the other hand, pre-trained neural language models can generate stories with great diversity, while being generally incapable of ending a story in a speciﬁed manner and can have trouble maintaining coherence. In this paper, we present an approach to story plot generation that uniﬁes causal planning with neural language models. We propose to use com- monsense knowledge extracted from large language models to recursively expand a story plot in a backward chaining fashion. Speciﬁcally, our system infers the preconditions for events in the story and then events that will cause those con- ditions to become true. We performed automatic evaluation to measure narrative coherence as indicated by the ability to an- swer questions about whether different events in the story are causally related to other events. Results indicate that our pro- posed method produces more coherent plotlines than several strong baselines.