@inproceedings{gillin-2022-encoder,
    title = "Is Encoder-Decoder Transformer the Shiny Hammer?",
    author = "Gillin, Nat",
    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/fix-sig-urls/2022.vardial-1.9/",
    pages = "80--85",
    abstract = "We present an approach to multi-class classification using an encoder-decoder transformer model. We trained a network to identify French varieties using the same scripts we use to train an encoder-decoder machine translation model. With some slight modification to the data preparation and inference parameters, we showed that the same tools used for machine translation can be easily re-used to achieve competitive performance for classification. On the French Dialectal Identification (FDI) task, we scored 32.4 on weighted F1, but this is far from a simple naive bayes classifier that outperforms a neural encoder-decoder model at 41.27 weighted F1."
}