  Open-ended text generation tasks, such as dialogue generation and story
completion, require models to generate a coherent continuation given limited
preceding context. The open-ended nature of these tasks brings new challenges
to the neural auto-regressive text generators nowadays. Despite these neural
models are good at producing human-like text, it is difficult for them to
arrange causalities and relations between given facts and possible ensuing
events. To bridge this gap, we propose a novel two-stage method which
explicitly arranges the ensuing events in open-ended text generation. Our
approach can be understood as a specially-trained coarse-to-fine algorithm,
where an event transition planner provides a "coarse" plot skeleton and a text
generator in the second stage refines the skeleton. Experiments on two
open-ended text generation tasks demonstrate that our proposed method
effectively improves the quality of the generated text, especially in coherence
and diversity. The code is available at:
\url{https://github.com/qtli/EventPlanforTextGen}.
