  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.
