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        <title>Maintaining Common Ground in Dynamic Environments</title>
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    <name type="personal">
        <namePart type="given">Takuma</namePart>
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    <abstract>Common grounding is the process of creating and maintaining mutual understandings, which is a critical aspect of sophisticated human communication. While various task settings have been proposed in existing literature, they mostly focus on creating common ground under a static context and ignore the aspect of maintaining them overtime under dynamic context. In this work, we propose a novel task setting to study the ability of both creating and maintaining common ground in dynamic environments. Based on our minimal task formulation, we collected a large-scale dataset of 5,617 dialogues to enable fine-grained evaluation and analysis of various dialogue systems. Through our dataset analyses, we highlight novel challenges introduced in our setting, such as the usage of complex spatio-temporal expressions to create and maintain common ground. Finally, we conduct extensive experiments to assess the capabilities of our baseline dialogue system and discuss future prospects of our research.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00409</identifier>
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        <date>2021</date>
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%0 Journal Article
%T Maintaining Common Ground in Dynamic Environments
%A Udagawa, Takuma
%A Aizawa, Akiko
%J Transactions of the Association for Computational Linguistics
%D 2021
%V 9
%I MIT Press
%C Cambridge, MA
%F udagawa-aizawa-2021-maintaining
%X Common grounding is the process of creating and maintaining mutual understandings, which is a critical aspect of sophisticated human communication. While various task settings have been proposed in existing literature, they mostly focus on creating common ground under a static context and ignore the aspect of maintaining them overtime under dynamic context. In this work, we propose a novel task setting to study the ability of both creating and maintaining common ground in dynamic environments. Based on our minimal task formulation, we collected a large-scale dataset of 5,617 dialogues to enable fine-grained evaluation and analysis of various dialogue systems. Through our dataset analyses, we highlight novel challenges introduced in our setting, such as the usage of complex spatio-temporal expressions to create and maintain common ground. Finally, we conduct extensive experiments to assess the capabilities of our baseline dialogue system and discuss future prospects of our research.
%R 10.1162/tacl_a_00409
%U https://aclanthology.org/2021.tacl-1.59
%U https://doi.org/10.1162/tacl_a_00409
%P 995-1011

Markdown (Informal)

[Maintaining Common Ground in Dynamic Environments](https://aclanthology.org/2021.tacl-1.59) (Udagawa & Aizawa, TACL 2021)

• Maintaining Common Ground in Dynamic Environments (Udagawa & Aizawa, TACL 2021)

ACL

• Takuma Udagawa and Akiko Aizawa. 2021. Maintaining Common Ground in Dynamic Environments. Transactions of the Association for Computational Linguistics, 9:995–1011.