  Neural network based approaches to automated story plot generation attempt to
learn how to generate novel plots from a corpus of natural language plot
summaries. Prior work has shown that a semantic abstraction of sentences called
events improves neural plot generation and and allows one to decompose the
problem into: (1) the generation of a sequence of events (event-to-event) and
(2) the transformation of these events into natural language sentences
(event-to-sentence). However, typical neural language generation approaches to
event-to-sentence can ignore the event details and produce
grammatically-correct but semantically-unrelated sentences. We present an
ensemble-based model that generates natural language guided by events.We
provide results---including a human subjects study---for a full end-to-end
automated story generation system showing that our method generates more
coherent and plausible stories than baseline approaches.
