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.

翻译：本文介绍了塔图自然语言处理团队在EvaLatin 2024评测任务中的参赛方案，该任务针对历史拉丁语文本的情感极性检测。我们的系统采用两种不同的方法来标注用于监督学习的训练数据：1）采用组织者提供的情感极性词典，基于启发式规则生成标签；2）利用GPT4生成标签。我们采用适配器框架进行参数高效微调，并在训练语言适配器和任务适配器时，实验了单语言与跨语言知识迁移策略。基于大语言模型生成标签的参赛方案最终在情感极性检测任务中取得总体第一名。研究结果表明，大语言模型标注对拉丁语文本的处理展现出良好的效果。