Unsupervised Hierarchical Story Infilling

David Grangier
Chris Callison-Burch
NAACL 2019 Workshop on Narrative Understanding, Minneapolis, MN (2019)
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Abstract

Story infilling involves predicting words to go into a missing span from a story.
This challenging task has the potential to transform interactive tools for creative writing.
However, state-of-the-art conditional language models have trouble balancing fluency and coherence with novelty and diversity. We address this limitation with a hierarchical model which first selects a set of rare words and then generates text conditioned on that set. By relegating the high entropy task of picking rare words to a word-sampling model, the second-stage model conditioned on those words can achieve high fluency and coherence by searching for likely sentences, without sacrificing diversity.