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    <titleInfo>
        <title>Improving Bridging Reference Resolution using Continuous Essentiality from Crowdsourcing</title>
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    <name type="personal">
        <namePart type="given">Nobuhiro</namePart>
        <namePart type="family">Ueda</namePart>
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            <title>Proceedings of the Fifth Workshop on Computational Models of Reference, Anaphora and Coreference</title>
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        <name type="personal">
            <namePart type="given">Maciej</namePart>
            <namePart type="family">Ogrodniczuk</namePart>
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            <namePart type="given">Sameer</namePart>
            <namePart type="family">Pradhan</namePart>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Nedoluzhko</namePart>
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            <namePart type="given">Massimo</namePart>
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    <abstract>Bridging reference resolution is the task of finding nouns that complement essential information of another noun. The essentiality varies depending on noun combination and context and has a continuous distribution. Despite the continuous nature of essentiality, existing datasets of bridging reference have only a few coarse labels to represent the essentiality. In this work, we propose a crowdsourcing-based annotation method that considers continuous essentiality. In the crowdsourcing task, we asked workers to select both all nouns with a bridging reference relation and a noun with the highest essentiality among them. Combining these annotations, we can obtain continuous essentiality. Experimental results demonstrated that the constructed dataset improves bridging reference resolution performance. The code is available at https://github.com/nobu-g/bridging-resolution.</abstract>
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            <start>74</start>
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%0 Conference Proceedings
%T Improving Bridging Reference Resolution using Continuous Essentiality from Crowdsourcing
%A Ueda, Nobuhiro
%A Kurohashi, Sadao
%Y Ogrodniczuk, Maciej
%Y Pradhan, Sameer
%Y Nedoluzhko, Anna
%Y Ng, Vincent
%Y Poesio, Massimo
%S Proceedings of the Fifth Workshop on Computational Models of Reference, Anaphora and Coreference
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F ueda-kurohashi-2022-improving
%X Bridging reference resolution is the task of finding nouns that complement essential information of another noun. The essentiality varies depending on noun combination and context and has a continuous distribution. Despite the continuous nature of essentiality, existing datasets of bridging reference have only a few coarse labels to represent the essentiality. In this work, we propose a crowdsourcing-based annotation method that considers continuous essentiality. In the crowdsourcing task, we asked workers to select both all nouns with a bridging reference relation and a noun with the highest essentiality among them. Combining these annotations, we can obtain continuous essentiality. Experimental results demonstrated that the constructed dataset improves bridging reference resolution performance. The code is available at https://github.com/nobu-g/bridging-resolution.
%U https://aclanthology.org/2022.crac-1.8/
%P 74-87

Markdown (Informal)

[Improving Bridging Reference Resolution using Continuous Essentiality from Crowdsourcing](https://aclanthology.org/2022.crac-1.8/) (Ueda & Kurohashi, CRAC 2022)

• Improving Bridging Reference Resolution using Continuous Essentiality from Crowdsourcing (Ueda & Kurohashi, CRAC 2022)

ACL

• Nobuhiro Ueda and Sadao Kurohashi. 2022. Improving Bridging Reference Resolution using Continuous Essentiality from Crowdsourcing. In Proceedings of the Fifth Workshop on Computational Models of Reference, Anaphora and Coreference, pages 74–87, Gyeongju, Republic of Korea. Association for Computational Linguistics.