@inproceedings{ippolito-etal-2019-unsupervised,
title = "Unsupervised Hierarchical Story Infilling",
author = "Ippolito, Daphne and
Grangier, David and
Callison-Burch, Chris and
Eck, Douglas",
editor = "Bamman, David and
Chaturvedi, Snigdha and
Clark, Elizabeth and
Fiterau, Madalina and
Iyyer, Mohit",
booktitle = "Proceedings of the First Workshop on Narrative Understanding",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2405",
doi = "10.18653/v1/W19-2405",
pages = "37--43",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ippolito-etal-2019-unsupervised">
<titleInfo>
<title>Unsupervised Hierarchical Story Infilling</title>
</titleInfo>
<name type="personal">
<namePart type="given">Daphne</namePart>
<namePart type="family">Ippolito</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Grangier</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chris</namePart>
<namePart type="family">Callison-Burch</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Douglas</namePart>
<namePart type="family">Eck</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2019-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the First Workshop on Narrative Understanding</title>
</titleInfo>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Bamman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Snigdha</namePart>
<namePart type="family">Chaturvedi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elizabeth</namePart>
<namePart type="family">Clark</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Madalina</namePart>
<namePart type="family">Fiterau</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mohit</namePart>
<namePart type="family">Iyyer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Minneapolis, Minnesota</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">ippolito-etal-2019-unsupervised</identifier>
<identifier type="doi">10.18653/v1/W19-2405</identifier>
<location>
<url>https://aclanthology.org/W19-2405</url>
</location>
<part>
<date>2019-06</date>
<extent unit="page">
<start>37</start>
<end>43</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Unsupervised Hierarchical Story Infilling
%A Ippolito, Daphne
%A Grangier, David
%A Callison-Burch, Chris
%A Eck, Douglas
%Y Bamman, David
%Y Chaturvedi, Snigdha
%Y Clark, Elizabeth
%Y Fiterau, Madalina
%Y Iyyer, Mohit
%S Proceedings of the First Workshop on Narrative Understanding
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F ippolito-etal-2019-unsupervised
%X 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.
%R 10.18653/v1/W19-2405
%U https://aclanthology.org/W19-2405
%U https://doi.org/10.18653/v1/W19-2405
%P 37-43
Markdown (Informal)
[Unsupervised Hierarchical Story Infilling](https://aclanthology.org/W19-2405) (Ippolito et al., WNU 2019)
ACL
- Daphne Ippolito, David Grangier, Chris Callison-Burch, and Douglas Eck. 2019. Unsupervised Hierarchical Story Infilling. In Proceedings of the First Workshop on Narrative Understanding, pages 37–43, Minneapolis, Minnesota. Association for Computational Linguistics.