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        <title>Applying Multilingual and Monolingual Transformer-Based Models for Dialect Identification</title>
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        <namePart type="given">Cristian</namePart>
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    <name type="personal">
        <namePart type="given">Vlad</namePart>
        <namePart type="family">\textcommabelowStefănescu</namePart>
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            <title>Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects</title>
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            <namePart type="given">Marcos</namePart>
            <namePart type="family">Zampieri</namePart>
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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">Yves</namePart>
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    <abstract>We study the ability of large fine-tuned transformer models to solve a binary classification task of dialect identification, with a special interest in comparing the performance of multilingual to monolingual ones. The corpus analyzed contains Romanian and Moldavian samples from the news domain, as well as tweets for assessing the performance. We find that the monolingual models are superior to the multilingual ones and the best results are obtained using an SVM ensemble of 5 different transformer-based models. We provide our experimental results and an analysis of the attention mechanisms of the best-performing individual classifiers to explain their decisions. The code we used was released under an open-source license.</abstract>
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%0 Conference Proceedings
%T Applying Multilingual and Monolingual Transformer-Based Models for Dialect Identification
%A Popa, Cristian
%A \textcommabelowStefănescu, Vlad
%Y Zampieri, Marcos
%Y Nakov, Preslav
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Scherrer, Yves
%S Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2020
%8 December
%I International Committee on Computational Linguistics (ICCL)
%C Barcelona, Spain (Online)
%F popa-stefanescu-2020-applying
%X We study the ability of large fine-tuned transformer models to solve a binary classification task of dialect identification, with a special interest in comparing the performance of multilingual to monolingual ones. The corpus analyzed contains Romanian and Moldavian samples from the news domain, as well as tweets for assessing the performance. We find that the monolingual models are superior to the multilingual ones and the best results are obtained using an SVM ensemble of 5 different transformer-based models. We provide our experimental results and an analysis of the attention mechanisms of the best-performing individual classifiers to explain their decisions. The code we used was released under an open-source license.
%U https://aclanthology.org/2020.vardial-1.18
%P 193-201

Markdown (Informal)

[Applying Multilingual and Monolingual Transformer-Based Models for Dialect Identification](https://aclanthology.org/2020.vardial-1.18) (Popa & Ștefănescu, VarDial 2020)

• Applying Multilingual and Monolingual Transformer-Based Models for Dialect Identification (Popa & Ștefănescu, VarDial 2020)

ACL

• Cristian Popa and Vlad Ștefănescu. 2020. Applying Multilingual and Monolingual Transformer-Based Models for Dialect Identification. In Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects, pages 193–201, Barcelona, Spain (Online). International Committee on Computational Linguistics (ICCL).