Writers generally rely on plans or sketches to write long stories, but most
current language models generate word by word from left to right. We explore
coarse-to-fine models for creating narrative texts of several hundred words,
and introduce new models which decompose stories by abstracting over actions
and entities. The model first generates the predicate-argument structure of the
text, where different mentions of the same entity are marked with placeholder
tokens. It then generates a surface realization of the predicate-argument
structure, and finally replaces the entity placeholders with context-sensitive
names and references. Human judges prefer the stories from our models to a wide
range of previous approaches to hierarchical text generation. Extensive
analysis shows that our methods can help improve the diversity and coherence of
events and entities in generated stories.

本研究探索了一种新型的粗粒度到细粒度的模型，可以生成故事文本，并通过抽象行为和实体来分解故事，生成谓词 - 参数结构的文本，最终替换实体占位符。经过广泛的分析和人工评估，我们的方法可以提高生成的故事中事件和实体的多样性和连贯性。