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- W2963614567 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." @default.
- W2963614567 created "2019-07-30" @default.
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- W2963614567 date "2019-01-01" @default.
- W2963614567 modified "2023-09-26" @default.
- W2963614567 title "Unsupervised Hierarchical Story Infilling" @default.
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- W2963614567 doi "https://doi.org/10.18653/v1/w19-2405" @default.
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