@inproceedings{dorkin-sirts-2024-tartunlp,
    title = "{T}artu{NLP} @ {SIGTYP} 2024 Shared Task: Adapting {XLM}-{R}o{BERT}a for Ancient and Historical Languages",
    author = "Dorkin, Aleksei  and
      Sirts, Kairit",
    editor = "Hahn, Michael  and
      Sorokin, Alexey  and
      Kumar, Ritesh  and
      Shcherbakov, Andreas  and
      Otmakhova, Yulia  and
      Yang, Jinrui  and
      Serikov, Oleg  and
      Rani, Priya  and
      Ponti, Edoardo M.  and
      Murado{\u{g}}lu, Saliha  and
      Gao, Rena  and
      Cotterell, Ryan  and
      Vylomova, Ekaterina",
    booktitle = "Proceedings of the 6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP",
    month = mar,
    year = "2024",
    address = "St. Julian's, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.sigtyp-1.15/",
    pages = "120--130",
    abstract = "We present our submission to the unconstrained subtask of the SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages for morphological annotation, POS-tagging, lemmatization, characterand word-level gap-filling. We developed a simple, uniform, and computationally lightweight approach based on the adapters framework using parameter-efficient fine-tuning. We applied the same adapter-based approach uniformly to all tasks and 16 languages by fine-tuning stacked language- and task-specific adapters. Our submission obtained an overall second place out of three submissions, with the first place in word-level gap-filling. Our results show the feasibility of adapting language models pre-trained on modern languages to historical and ancient languages via adapter training."
}