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    <titleInfo>
        <title>Native Language Prediction from Gaze: a Reproducibility Study</title>
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    <name type="personal">
        <namePart type="given">Lina</namePart>
        <namePart type="family">Skerath</namePart>
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    <name type="personal">
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Maria</namePart>
        <namePart type="family">Barrett</namePart>
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    <name type="personal">
        <namePart type="given">Rob</namePart>
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            <title>Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)</title>
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        <name type="personal">
            <namePart type="given">Vishakh</namePart>
            <namePart type="family">Padmakumar</namePart>
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        <name type="personal">
            <namePart type="given">Gisela</namePart>
            <namePart type="family">Vallejo</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Yao</namePart>
            <namePart type="family">Fu</namePart>
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    <abstract>Numerous studies found that the linguistic properties of a person’s native language affect the cognitive processing of other languages. However, only one study has shown that it was possible to identify the native language based on eye-tracking records of natural L2 reading using machine learning. A new corpus allows us to replicate these results on a more interrelated and larger set of native languages. Our results show that comparable classification performance is maintained despite using less data. However, analysis shows that the correlation between L2 eye movements and native language similarity may be more complex than the original study found.</abstract>
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    <identifier type="doi">10.18653/v1/2023.acl-srw.26</identifier>
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        <date>2023-07</date>
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%0 Conference Proceedings
%T Native Language Prediction from Gaze: a Reproducibility Study
%A Skerath, Lina
%A Toborek, Paulina
%A Zielińska, Anita
%A Barrett, Maria
%A Van Der Goot, Rob
%Y Padmakumar, Vishakh
%Y Vallejo, Gisela
%Y Fu, Yao
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F skerath-etal-2023-native
%X Numerous studies found that the linguistic properties of a person’s native language affect the cognitive processing of other languages. However, only one study has shown that it was possible to identify the native language based on eye-tracking records of natural L2 reading using machine learning. A new corpus allows us to replicate these results on a more interrelated and larger set of native languages. Our results show that comparable classification performance is maintained despite using less data. However, analysis shows that the correlation between L2 eye movements and native language similarity may be more complex than the original study found.
%R 10.18653/v1/2023.acl-srw.26
%U https://aclanthology.org/2023.acl-srw.26
%U https://doi.org/10.18653/v1/2023.acl-srw.26
%P 152-159

Markdown (Informal)

[Native Language Prediction from Gaze: a Reproducibility Study](https://aclanthology.org/2023.acl-srw.26) (Skerath et al., ACL 2023)

• Native Language Prediction from Gaze: a Reproducibility Study (Skerath et al., ACL 2023)

ACL

• Lina Skerath, Paulina Toborek, Anita Zielińska, Maria Barrett, and Rob Van Der Goot. 2023. Native Language Prediction from Gaze: a Reproducibility Study. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 152–159, Toronto, Canada. Association for Computational Linguistics.