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    <titleInfo>
        <title>Talking about the world with a distributed model</title>
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    <name type="personal">
        <namePart type="given">Gemma</namePart>
        <namePart type="family">Boleda</namePart>
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            <title>Proceedings of the 10th International Conference on Natural Language Generation</title>
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        <name type="personal">
            <namePart type="given">Jose</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Alonso</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Alberto</namePart>
            <namePart type="family">Bugarín</namePart>
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            <namePart type="given">Ehud</namePart>
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    <abstract>We use language to talk about the world, and so reference is a crucial property of language. However, modeling reference is particularly difficult, as it involves both continuous and discrete as-pects of language. For instance, referring expressions like “the big mug” or “it” typically contain content words (“big”, “mug”), which are notoriously fuzzy or vague in their meaning, and also fun-ction words (“the”, “it”) that largely serve as discrete pointers. Data-driven, distributed models based on distributional semantics or deep learning excel at the former, but struggle with the latter, and the reverse is true for symbolic models. I present ongoing work on modeling reference with a distribu-ted model aimed at capturing both aspects, and learns to refer directly from reference acts.</abstract>
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    <identifier type="doi">10.18653/v1/W17-3515</identifier>
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%0 Conference Proceedings
%T Talking about the world with a distributed model
%A Boleda, Gemma
%Y Alonso, Jose M.
%Y Bugarín, Alberto
%Y Reiter, Ehud
%S Proceedings of the 10th International Conference on Natural Language Generation
%D 2017
%8 September
%I Association for Computational Linguistics
%C Santiago de Compostela, Spain
%F boleda-2017-talking
%X We use language to talk about the world, and so reference is a crucial property of language. However, modeling reference is particularly difficult, as it involves both continuous and discrete as-pects of language. For instance, referring expressions like “the big mug” or “it” typically contain content words (“big”, “mug”), which are notoriously fuzzy or vague in their meaning, and also fun-ction words (“the”, “it”) that largely serve as discrete pointers. Data-driven, distributed models based on distributional semantics or deep learning excel at the former, but struggle with the latter, and the reverse is true for symbolic models. I present ongoing work on modeling reference with a distribu-ted model aimed at capturing both aspects, and learns to refer directly from reference acts.
%R 10.18653/v1/W17-3515
%U https://aclanthology.org/W17-3515
%U https://doi.org/10.18653/v1/W17-3515
%P 114

Markdown (Informal)

[Talking about the world with a distributed model](https://aclanthology.org/W17-3515) (Boleda, INLG 2017)

• Talking about the world with a distributed model (Boleda, INLG 2017)

ACL

• Gemma Boleda. 2017. Talking about the world with a distributed model. In Proceedings of the 10th International Conference on Natural Language Generation, page 114, Santiago de Compostela, Spain. Association for Computational Linguistics.