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    <titleInfo>
        <title>Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction</title>
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    <name type="personal">
        <namePart type="given">Ashutosh</namePart>
        <namePart type="family">Modi</namePart>
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    <name type="personal">
        <namePart type="given">Ivan</namePart>
        <namePart type="family">Titov</namePart>
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    <name type="personal">
        <namePart type="given">Vera</namePart>
        <namePart type="family">Demberg</namePart>
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    <name type="personal">
        <namePart type="given">Asad</namePart>
        <namePart type="family">Sayeed</namePart>
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    <name type="personal">
        <namePart type="given">Manfred</namePart>
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    <abstract>Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents based on linguistic knowledge alone vs. linguistic knowledge jointly with common-sense knowledge in the form of scripts. We find that script knowledge significantly improves model estimates of human predictions. In a second study, we test the highly controversial hypothesis that predictability influences referring expression type but do not find evidence for such an effect.</abstract>
    <identifier type="citekey">modi-etal-2017-modeling</identifier>
    <identifier type="doi">10.1162/tacl_a_00044</identifier>
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        <url>https://aclanthology.org/Q17-1003/</url>
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        <date>2017</date>
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            <start>31</start>
            <end>44</end>
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%0 Journal Article
%T Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction
%A Modi, Ashutosh
%A Titov, Ivan
%A Demberg, Vera
%A Sayeed, Asad
%A Pinkal, Manfred
%J Transactions of the Association for Computational Linguistics
%D 2017
%V 5
%I MIT Press
%C Cambridge, MA
%F modi-etal-2017-modeling
%X Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents based on linguistic knowledge alone vs. linguistic knowledge jointly with common-sense knowledge in the form of scripts. We find that script knowledge significantly improves model estimates of human predictions. In a second study, we test the highly controversial hypothesis that predictability influences referring expression type but do not find evidence for such an effect.
%R 10.1162/tacl_a_00044
%U https://aclanthology.org/Q17-1003/
%U https://doi.org/10.1162/tacl_a_00044
%P 31-44

Markdown (Informal)

[Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction](https://aclanthology.org/Q17-1003/) (Modi et al., TACL 2017)

• Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction (Modi et al., TACL 2017)

ACL

• Ashutosh Modi, Ivan Titov, Vera Demberg, Asad Sayeed, and Manfred Pinkal. 2017. Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction. Transactions of the Association for Computational Linguistics, 5:31–44.