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        <title>Narrative Modeling with Memory Chains and Semantic Supervision</title>
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    <name type="personal">
        <namePart type="given">Fei</namePart>
        <namePart type="family">Liu</namePart>
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    <name type="personal">
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        </name>
        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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    <abstract>Story comprehension requires a deep semantic understanding of the narrative, making it a challenging task. Inspired by previous studies on ROC Story Cloze Test, we propose a novel method, tracking various semantic aspects with external neural memory chains while encouraging each to focus on a particular semantic aspect. Evaluated on the task of story ending prediction, our model demonstrates superior performance to a collection of competitive baselines, setting a new state of the art.</abstract>
    <identifier type="citekey">liu-etal-2018-narrative</identifier>
    <identifier type="doi">10.18653/v1/P18-2045</identifier>
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%0 Conference Proceedings
%T Narrative Modeling with Memory Chains and Semantic Supervision
%A Liu, Fei
%A Cohn, Trevor
%A Baldwin, Timothy
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F liu-etal-2018-narrative
%X Story comprehension requires a deep semantic understanding of the narrative, making it a challenging task. Inspired by previous studies on ROC Story Cloze Test, we propose a novel method, tracking various semantic aspects with external neural memory chains while encouraging each to focus on a particular semantic aspect. Evaluated on the task of story ending prediction, our model demonstrates superior performance to a collection of competitive baselines, setting a new state of the art.
%R 10.18653/v1/P18-2045
%U https://aclanthology.org/P18-2045
%U https://doi.org/10.18653/v1/P18-2045
%P 278-284

Markdown (Informal)

[Narrative Modeling with Memory Chains and Semantic Supervision](https://aclanthology.org/P18-2045) (Liu et al., ACL 2018)

• Narrative Modeling with Memory Chains and Semantic Supervision (Liu et al., ACL 2018)

ACL

• Fei Liu, Trevor Cohn, and Timothy Baldwin. 2018. Narrative Modeling with Memory Chains and Semantic Supervision. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 278–284, Melbourne, Australia. Association for Computational Linguistics.