  This paper presents the TartuNLP team submission to EvaLatin 2024 shared task
of the emotion polarity detection for historical Latin texts. Our system relies
on two distinct approaches to annotating training data for supervised learning:
1) creating heuristics-based labels by adopting the polarity lexicon provided
by the organizers and 2) generating labels with GPT4. We employed parameter
efficient fine-tuning using the adapters framework and experimented with both
monolingual and cross-lingual knowledge transfer for training language and task
adapters. Our submission with the LLM-generated labels achieved the overall
first place in the emotion polarity detection task. Our results show that
LLM-based annotations show promising results on texts in Latin.
