@inproceedings{bernier-colborne-etal-2022-transfer,
    title = "Transfer Learning Improves {F}rench Cross-Domain Dialect Identification: {NRC} @ {V}ar{D}ial 2022",
    author = "Bernier-Colborne, Gabriel  and
      Leger, Serge  and
      Goutte, Cyril",
    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 = "Proceedings of the Ninth Workshop on NLP for Similar Languages, Varieties and Dialects",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.vardial-1.12/",
    pages = "109--118",
    abstract = "We describe the systems developed by the National Research Council Canada for the French Cross-Domain Dialect Identification shared task at the 2022 VarDial evaluation campaign. We evaluated two different approaches to this task: SVM and probabilistic classifiers exploiting n-grams as features, and trained from scratch on the data provided; and a pre-trained French language model, CamemBERT, that we fine-tuned on the dialect identification task. The latter method turned out to improve the macro-F1 score on the test set from 0.344 to 0.430 (25{\%} increase), which indicates that transfer learning can be helpful for dialect identification."
}