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        <title>Byte-based Language Identification with Deep Convolutional Networks</title>
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        <namePart type="given">Johannes</namePart>
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            <title>Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)</title>
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        <name type="personal">
            <namePart type="given">Preslav</namePart>
            <namePart type="family">Nakov</namePart>
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        <name type="personal">
            <namePart type="given">Marcos</namePart>
            <namePart type="family">Zampieri</namePart>
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        <name type="personal">
            <namePart type="given">Liling</namePart>
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        <name type="personal">
            <namePart type="given">Nikola</namePart>
            <namePart type="family">Ljubešić</namePart>
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            <namePart type="given">Shervin</namePart>
            <namePart type="family">Malmasi</namePart>
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    <abstract>We report on our system for the shared task on discriminating between similar languages (DSL 2016). The system uses only byte representations in a deep residual network (ResNet). The system, named ResIdent, is trained only on the data released with the task (closed training). We obtain 84.88% accuracy on subtask A, 68.80% accuracy on subtask B1, and 69.80% accuracy on subtask B2. A large difference in accuracy on development data can be observed with relatively minor changes in our network’s architecture and hyperparameters. We therefore expect fine-tuning of these parameters to yield higher accuracies.</abstract>
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%0 Conference Proceedings
%T Byte-based Language Identification with Deep Convolutional Networks
%A Bjerva, Johannes
%Y Nakov, Preslav
%Y Zampieri, Marcos
%Y Tan, Liling
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Malmasi, Shervin
%S Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F bjerva-2016-byte
%X We report on our system for the shared task on discriminating between similar languages (DSL 2016). The system uses only byte representations in a deep residual network (ResNet). The system, named ResIdent, is trained only on the data released with the task (closed training). We obtain 84.88% accuracy on subtask A, 68.80% accuracy on subtask B1, and 69.80% accuracy on subtask B2. A large difference in accuracy on development data can be observed with relatively minor changes in our network’s architecture and hyperparameters. We therefore expect fine-tuning of these parameters to yield higher accuracies.
%U https://aclanthology.org/W16-4816/
%P 119-125

Markdown (Informal)

[Byte-based Language Identification with Deep Convolutional Networks](https://aclanthology.org/W16-4816/) (Bjerva, VarDial 2016)

• Byte-based Language Identification with Deep Convolutional Networks (Bjerva, VarDial 2016)

ACL

• Johannes Bjerva. 2016. Byte-based Language Identification with Deep Convolutional Networks. In Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3), pages 119–125, Osaka, Japan. The COLING 2016 Organizing Committee.