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arXiv · 1902.01109

Strategies for Structuring Story Generation

Angela Fan·Mike Lewis·Yann Dauphin
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Abstract

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.

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Angela Fan, Mike Lewis, Yann Dauphin. 2019-06-15. Strategies for Structuring Story Generation. https://arxiv.org/abs/1902.01109

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