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        <title>Towards Neuro-Symbolic Approaches for Referring Expression Generation</title>
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    <name type="personal">
        <namePart type="given">Manar</namePart>
        <namePart type="family">Ali</namePart>
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    <name type="personal">
        <namePart type="given">Marika</namePart>
        <namePart type="family">Sarzotti</namePart>
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    <name type="personal">
        <namePart type="given">Simeon</namePart>
        <namePart type="family">Junker</namePart>
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    <name type="personal">
        <namePart type="given">Hendrik</namePart>
        <namePart type="family">Buschmeier</namePart>
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    <name type="personal">
        <namePart type="given">Sina</namePart>
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        <name type="personal">
            <namePart type="given">Nikolai</namePart>
            <namePart type="family">Ilinykh</namePart>
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        <name type="personal">
            <namePart type="given">Mattias</namePart>
            <namePart type="family">Appelgren</namePart>
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            <namePart type="given">Erik</namePart>
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    <abstract>Referring Expression Generation (REG) has a long-standing tradition in computational linguistics, and often aims to develop cognitively plausible models of language generation and dialogue modeling, in a multimodal context. Traditional approaches to reference have been mostly symbolic, recent ones have been mostly neural. Inspired by the recent interest in neuro-symbolic approaches in both fields – language and vision – we revisit REG from these perspectives. We review relevant neuro-symbolic approaches to language generation on the one hand and vision on the other hand, exploring possible future directions for cognitively plausible models of reference generation/reference game modeling.</abstract>
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%0 Conference Proceedings
%T Towards Neuro-Symbolic Approaches for Referring Expression Generation
%A Ali, Manar
%A Sarzotti, Marika
%A Junker, Simeon
%A Buschmeier, Hendrik
%A Zarrieß, Sina
%Y Ilinykh, Nikolai
%Y Appelgren, Mattias
%Y Lagerstedt, Erik
%S Proceedings of the 2025 CLASP Conference on Language models And RePresentations (LARP)
%D 2025
%8 September
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%@ 979-8-89176-249-7
%F ali-etal-2025-towards
%X Referring Expression Generation (REG) has a long-standing tradition in computational linguistics, and often aims to develop cognitively plausible models of language generation and dialogue modeling, in a multimodal context. Traditional approaches to reference have been mostly symbolic, recent ones have been mostly neural. Inspired by the recent interest in neuro-symbolic approaches in both fields – language and vision – we revisit REG from these perspectives. We review relevant neuro-symbolic approaches to language generation on the one hand and vision on the other hand, exploring possible future directions for cognitively plausible models of reference generation/reference game modeling.
%U https://aclanthology.org/2025.clasp-main.4/
%P 38-50

Markdown (Informal)

[Towards Neuro-Symbolic Approaches for Referring Expression Generation](https://aclanthology.org/2025.clasp-main.4/) (Ali et al., CLASP 2025)

• Towards Neuro-Symbolic Approaches for Referring Expression Generation (Ali et al., CLASP 2025)

ACL

• Manar Ali, Marika Sarzotti, Simeon Junker, Hendrik Buschmeier, and Sina Zarrieß. 2025. Towards Neuro-Symbolic Approaches for Referring Expression Generation. In Proceedings of the 2025 CLASP Conference on Language models And RePresentations (LARP), pages 38–50, Gothenburg, Sweden. Association for Computational Linguistics.