@inproceedings{vamvas-etal-2024-modular,
    title = "Modular Adaptation of Multilingual Encoders to Written {S}wiss {G}erman Dialect",
    author = {Vamvas, Jannis  and
      Aepli, No{\"e}mi  and
      Sennrich, Rico},
    editor = {V{\'a}zquez, Ra{\'u}l  and
      Mickus, Timothee  and
      Tiedemann, J{\"o}rg  and
      Vuli{\'c}, Ivan  and
      {\"U}st{\"u}n, Ahmet},
    booktitle = "Proceedings of the 1st Workshop on Modular and Open Multilingual NLP (MOOMIN 2024)",
    month = mar,
    year = "2024",
    address = "St Julians, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2024.moomin-1.3/",
    pages = "16--23",
    abstract = "Creating neural text encoders for written Swiss German is challenging due to a dearth of training data combined with dialectal variation. In this paper, we build on several existing multilingual encoders and adapt them to Swiss German using continued pre-training. Evaluation on three diverse downstream tasks shows that simply adding a Swiss German adapter to a modular encoder achieves 97.5{\%} of fully monolithic adaptation performance. We further find that for the task of retrieving Swiss German sentences given Standard German queries, adapting a character-level model is more effective than the other adaptation strategies. We release our code and the models trained for our experiments."
}