@inproceedings{hohl-shim-2023-vardial,
    title = "{V}ar{D}ial in the Wild: Industrial Applications of {LID} Systems for Closely-Related Language Varieties",
    author = "Hohl, Fritz  and
      Shim, Soh-eun",
    editor = {Scherrer, Yves  and
      Jauhiainen, Tommi  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Nakov, Preslav  and
      Tiedemann, J{\"o}rg  and
      Zampieri, Marcos},
    booktitle = "Tenth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2023)",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.vardial-1.21/",
    doi = "10.18653/v1/2023.vardial-1.21",
    pages = "213--221",
    abstract = "This report describes first an industrial use case for identifying closely related languages, e.g.dialects, namely the detection of languages of movie subtitle documents. We then presenta 2-stage architecture that is able to detect macrolanguages in the first stage and languagevariants in the second. Using our architecture, we participated in the DSL-TL Shared Task of the VarDial 2023 workshop. We describe the results of our experiments. In the first experiment we report an accuracy of 97.8{\%} on a set of 460 subtitle files. In our second experimentwe used DSL-TL data and achieve a macroaverage F1 of 76{\%} for the binary task, and 54{\%} for the three-way task in the dev set. In the open track, we augment the data with named entities retrieved from Wikidata and achieve minor increases of about 1{\%} for both tracks."
}