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    <titleInfo>
        <title>Identifying Sensible Lexical Relations in Generated Stories</title>
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        <namePart type="given">Melissa</namePart>
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            <title>Proceedings of the First Workshop on Narrative Understanding</title>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Bamman</namePart>
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        <name type="personal">
            <namePart type="given">Snigdha</namePart>
            <namePart type="family">Chaturvedi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        </name>
        <name type="personal">
            <namePart type="given">Elizabeth</namePart>
            <namePart type="family">Clark</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Madalina</namePart>
            <namePart type="family">Fiterau</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Mohit</namePart>
            <namePart type="family">Iyyer</namePart>
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    <abstract>As with many text generation tasks, the focus of recent progress on story generation has been in producing texts that are perceived to “make sense” as a whole. There are few automated metrics that address this dimension of story quality even on a shallow lexical level. To initiate investigation into such metrics, we apply a simple approach to identifying word relations that contribute to the ‘narrative sense’ of a story. We use this approach to comparatively analyze the output of a few notable story generation systems in terms of these relations. We characterize differences in the distributions of relations according to their strength within each story.</abstract>
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    <identifier type="doi">10.18653/v1/W19-2406</identifier>
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%0 Conference Proceedings
%T Identifying Sensible Lexical Relations in Generated Stories
%A Roemmele, Melissa
%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 roemmele-2019-identifying
%X As with many text generation tasks, the focus of recent progress on story generation has been in producing texts that are perceived to “make sense” as a whole. There are few automated metrics that address this dimension of story quality even on a shallow lexical level. To initiate investigation into such metrics, we apply a simple approach to identifying word relations that contribute to the ‘narrative sense’ of a story. We use this approach to comparatively analyze the output of a few notable story generation systems in terms of these relations. We characterize differences in the distributions of relations according to their strength within each story.
%R 10.18653/v1/W19-2406
%U https://aclanthology.org/W19-2406/
%U https://doi.org/10.18653/v1/W19-2406
%P 44-52

Markdown (Informal)

[Identifying Sensible Lexical Relations in Generated Stories](https://aclanthology.org/W19-2406/) (Roemmele, WNU 2019)

• Identifying Sensible Lexical Relations in Generated Stories (Roemmele, WNU 2019)

ACL

• Melissa Roemmele. 2019. Identifying Sensible Lexical Relations in Generated Stories. In Proceedings of the First Workshop on Narrative Understanding, pages 44–52, Minneapolis, Minnesota. Association for Computational Linguistics.