Unsupervised Hierarchical Story Infilling

Daphne Ippolito, David Grangier, Chris Callison-Burch, Douglas Eck


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.
Anthology ID:
W19-2405
Volume:
Proceedings of the First Workshop on Narrative Understanding
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venues:
NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
37–43
Language:
URL:
https://www.aclweb.org/anthology/W19-2405
DOI:
10.18653/v1/W19-2405
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-2405.pdf