@inproceedings{stefanik-etal-2022-adaptor,
    title = "Adaptor: Objective-Centric Adaptation Framework for Language Models",
    author = "{\v{S}}tef{\'a}nik, Michal  and
      Novotn{\'y}, V{\'i}t  and
      Groverov{\'a}, Nikola  and
      Sojka, Petr",
    editor = "Basile, Valerio  and
      Kozareva, Zornitsa  and
      Stajner, Sanja",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.acl-demo.26/",
    doi = "10.18653/v1/2022.acl-demo.26",
    pages = "261--269",
    abstract = "This paper introduces Adaptor library, which transposes traditional model-centric approach composed of pre-training + fine-tuning steps to objective-centric approach, composing the training process by applications of selected objectives. We survey research directions that can benefit from enhanced objective-centric experimentation in multitask training, custom objectives development, dynamic training curricula, or domain adaptation. Adaptor aims to ease reproducibility of these research directions in practice. Finally, we demonstrate the practical applicability of Adaptor in selected unsupervised domain adaptation scenarios."
}