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        <title>A Deep Generative Approach to Native Language Identification</title>
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        <namePart type="given">Ehsan</namePart>
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    <name type="personal">
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        <namePart type="family">Markov</namePart>
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    <name type="personal">
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            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
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        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
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            <namePart type="given">Chengqing</namePart>
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    <abstract>Native language identification (NLI) – identifying the native language (L1) of a person based on his/her writing in the second language (L2) – is useful for a variety of purposes, including marketing, security, and educational applications. From a traditional machine learning perspective,NLI is usually framed as a multi-class classification task, where numerous designed features are combined in order to achieve the state-of-the-art results. We introduce a deep generative language modelling (LM) approach to NLI, which consists in fine-tuning a GPT-2 model separately on texts written by the authors with the same L1, and assigning a label to an unseen text based on the minimum LM loss with respect to one of these fine-tuned GPT-2 models. Our method outperforms traditional machine learning approaches and currently achieves the best results on the benchmark NLI datasets.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.159</identifier>
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        <date>2020-12</date>
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%0 Conference Proceedings
%T A Deep Generative Approach to Native Language Identification
%A Lotfi, Ehsan
%A Markov, Ilia
%A Daelemans, Walter
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F lotfi-etal-2020-deep
%X Native language identification (NLI) – identifying the native language (L1) of a person based on his/her writing in the second language (L2) – is useful for a variety of purposes, including marketing, security, and educational applications. From a traditional machine learning perspective,NLI is usually framed as a multi-class classification task, where numerous designed features are combined in order to achieve the state-of-the-art results. We introduce a deep generative language modelling (LM) approach to NLI, which consists in fine-tuning a GPT-2 model separately on texts written by the authors with the same L1, and assigning a label to an unseen text based on the minimum LM loss with respect to one of these fine-tuned GPT-2 models. Our method outperforms traditional machine learning approaches and currently achieves the best results on the benchmark NLI datasets.
%R 10.18653/v1/2020.coling-main.159
%U https://aclanthology.org/2020.coling-main.159
%U https://doi.org/10.18653/v1/2020.coling-main.159
%P 1778-1783

Markdown (Informal)

[A Deep Generative Approach to Native Language Identification](https://aclanthology.org/2020.coling-main.159) (Lotfi et al., COLING 2020)

• A Deep Generative Approach to Native Language Identification (Lotfi et al., COLING 2020)

ACL

• Ehsan Lotfi, Ilia Markov, and Walter Daelemans. 2020. A Deep Generative Approach to Native Language Identification. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1778–1783, Barcelona, Spain (Online). International Committee on Computational Linguistics.