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        <title>German Dialect Identification in Interview Transcriptions</title>
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            <title>Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)</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">Nikola</namePart>
            <namePart type="family">Ljubešić</namePart>
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            <namePart type="given">Jörg</namePart>
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            <namePart type="given">Ahmed</namePart>
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    <abstract>This paper presents three systems submitted to the German Dialect Identification (GDI) task at the VarDial Evaluation Campaign 2017. The task consists of training models to identify the dialect of Swiss-German speech transcripts. The dialects included in the GDI dataset are Basel, Bern, Lucerne, and Zurich. The three systems we submitted are based on: a plurality ensemble, a mean probability ensemble, and a meta-classifier trained on character and word n-grams. The best results were obtained by the meta-classifier achieving 68.1% accuracy and 66.2% F1-score, ranking first among the 10 teams which participated in the GDI shared task.</abstract>
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%0 Conference Proceedings
%T German Dialect Identification in Interview Transcriptions
%A Malmasi, Shervin
%A Zampieri, Marcos
%Y Nakov, Preslav
%Y Zampieri, Marcos
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Malmasi, Shevin
%Y Ali, Ahmed
%S Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F malmasi-zampieri-2017-german
%X This paper presents three systems submitted to the German Dialect Identification (GDI) task at the VarDial Evaluation Campaign 2017. The task consists of training models to identify the dialect of Swiss-German speech transcripts. The dialects included in the GDI dataset are Basel, Bern, Lucerne, and Zurich. The three systems we submitted are based on: a plurality ensemble, a mean probability ensemble, and a meta-classifier trained on character and word n-grams. The best results were obtained by the meta-classifier achieving 68.1% accuracy and 66.2% F1-score, ranking first among the 10 teams which participated in the GDI shared task.
%R 10.18653/v1/W17-1220
%U https://aclanthology.org/W17-1220
%U https://doi.org/10.18653/v1/W17-1220
%P 164-169

Markdown (Informal)

[German Dialect Identification in Interview Transcriptions](https://aclanthology.org/W17-1220) (Malmasi & Zampieri, VarDial 2017)

• German Dialect Identification in Interview Transcriptions (Malmasi & Zampieri, VarDial 2017)

ACL

• Shervin Malmasi and Marcos Zampieri. 2017. German Dialect Identification in Interview Transcriptions. In Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial), pages 164–169, Valencia, Spain. Association for Computational Linguistics.