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        <title>Cross-corpus Native Language Identification via Statistical Embedding</title>
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        <namePart type="given">Francisco</namePart>
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    <name type="personal">
        <namePart type="given">Alexandra</namePart>
        <namePart type="family">Uitdenbogerd</namePart>
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            <title>Proceedings of the Second Workshop on Stylistic Variation</title>
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            <namePart type="given">Julian</namePart>
            <namePart type="family">Brooke</namePart>
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        <name type="personal">
            <namePart type="given">Lucie</namePart>
            <namePart type="family">Flekova</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Moshe</namePart>
            <namePart type="family">Koppel</namePart>
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        <name type="personal">
            <namePart type="given">Thamar</namePart>
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    <abstract>In this paper, we approach the task of native language identification in a realistic cross-corpus scenario where a model is trained with available data and has to predict the native language from data of a different corpus. The motivation behind this study is to investigate native language identification in the Australian academic scenario where a majority of students come from China, Indonesia, and Arabic-speaking nations. We have proposed a statistical embedding representation reporting a significant improvement over common single-layer approaches of the state of the art, identifying Chinese, Arabic, and Indonesian in a cross-corpus scenario. The proposed approach was shown to be competitive even when the data is scarce and imbalanced.</abstract>
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    <identifier type="doi">10.18653/v1/W18-1605</identifier>
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%0 Conference Proceedings
%T Cross-corpus Native Language Identification via Statistical Embedding
%A Rangel, Francisco
%A Rosso, Paolo
%A Brooke, Julian
%A Uitdenbogerd, Alexandra
%Y Brooke, Julian
%Y Flekova, Lucie
%Y Koppel, Moshe
%Y Solorio, Thamar
%S Proceedings of the Second Workshop on Stylistic Variation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans
%F rangel-etal-2018-cross
%X In this paper, we approach the task of native language identification in a realistic cross-corpus scenario where a model is trained with available data and has to predict the native language from data of a different corpus. The motivation behind this study is to investigate native language identification in the Australian academic scenario where a majority of students come from China, Indonesia, and Arabic-speaking nations. We have proposed a statistical embedding representation reporting a significant improvement over common single-layer approaches of the state of the art, identifying Chinese, Arabic, and Indonesian in a cross-corpus scenario. The proposed approach was shown to be competitive even when the data is scarce and imbalanced.
%R 10.18653/v1/W18-1605
%U https://aclanthology.org/W18-1605
%U https://doi.org/10.18653/v1/W18-1605
%P 39-43

Markdown (Informal)

[Cross-corpus Native Language Identification via Statistical Embedding](https://aclanthology.org/W18-1605) (Rangel et al., Style-Var 2018)

• Cross-corpus Native Language Identification via Statistical Embedding (Rangel et al., Style-Var 2018)

ACL

• Francisco Rangel, Paolo Rosso, Julian Brooke, and Alexandra Uitdenbogerd. 2018. Cross-corpus Native Language Identification via Statistical Embedding. In Proceedings of the Second Workshop on Stylistic Variation, pages 39–43, New Orleans. Association for Computational Linguistics.