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        <title>Analysis of Language Change in Collaborative Instruction Following</title>
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        <namePart type="given">Anna</namePart>
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    <name type="personal">
        <namePart type="given">Rhia</namePart>
        <namePart type="family">Singh</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Eva</namePart>
        <namePart type="family">Yan</namePart>
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    <name type="personal">
        <namePart type="given">Alane</namePart>
        <namePart type="family">Suhr</namePart>
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    <name type="personal">
        <namePart type="given">Yoav</namePart>
        <namePart type="family">Artzi</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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            <namePart type="given">Scott</namePart>
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            <namePart type="family">Yih</namePart>
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    <abstract>We analyze language change over time in a collaborative, goal-oriented instructional task, where utility-maximizing participants form conventions and increase their expertise. Prior work studied such scenarios mostly in the context of reference games, and consistently found that language complexity is reduced along multiple dimensions, such as utterance length, as conventions are formed. In contrast, we find that, given the ability to increase instruction utility, instructors increase language complexity along these previously studied dimensions to better collaborate with increasingly skilled instruction followers.</abstract>
    <identifier type="citekey">effenberger-etal-2021-analysis-language</identifier>
    <identifier type="doi">10.18653/v1/2021.findings-emnlp.239</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T Analysis of Language Change in Collaborative Instruction Following
%A Effenberger, Anna
%A Singh, Rhia
%A Yan, Eva
%A Suhr, Alane
%A Artzi, Yoav
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F effenberger-etal-2021-analysis-language
%X We analyze language change over time in a collaborative, goal-oriented instructional task, where utility-maximizing participants form conventions and increase their expertise. Prior work studied such scenarios mostly in the context of reference games, and consistently found that language complexity is reduced along multiple dimensions, such as utterance length, as conventions are formed. In contrast, we find that, given the ability to increase instruction utility, instructors increase language complexity along these previously studied dimensions to better collaborate with increasingly skilled instruction followers.
%R 10.18653/v1/2021.findings-emnlp.239
%U https://aclanthology.org/2021.findings-emnlp.239
%U https://doi.org/10.18653/v1/2021.findings-emnlp.239
%P 2803-2811

Markdown (Informal)

[Analysis of Language Change in Collaborative Instruction Following](https://aclanthology.org/2021.findings-emnlp.239) (Effenberger et al., Findings 2021)

• Analysis of Language Change in Collaborative Instruction Following (Effenberger et al., Findings 2021)

ACL

• Anna Effenberger, Rhia Singh, Eva Yan, Alane Suhr, and Yoav Artzi. 2021. Analysis of Language Change in Collaborative Instruction Following. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2803–2811, Punta Cana, Dominican Republic. Association for Computational Linguistics.