TartuNLP @ SIGTYP 2024 Shared Task: Adapting XLM-RoBERTa for Ancient and Historical Languages
<|reference_start|>TartuNLP @ SIGTYP 2024 Shared Task: Adapting XLM-RoBERTa for Ancient and Historical Languages: 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, character- and 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.<|reference_end|>
@article{dorkin2024tartunlp,
    title={TartuNLP @ SIGTYP 2024 Shared Task: Adapting XLM-RoBERTa for Ancient and
  Historical Languages},
    author={Aleksei Dorkin and Kairit Sirts},
    journal={Proceedings of the 6th Workshop on Research in Computational
  Linguistic Typology and Multilingual NLP, pp. 120-130, March 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12845},
    primaryClass={cs.CL}
}