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：本文介绍了塔尔图大学自然语言处理团队为EvaLatin 2024共享任务——历史拉丁文本情感极性检测——提交的系统。我们的系统采用两种不同的方法来标注监督学习所需的训练数据：1）利用组织者提供的极性词典创建基于启发式的标签；2）使用GPT4生成标签。我们采用适配器框架进行参数高效微调，并尝试了单语和跨语言知识迁移来训练语言适配器和任务适配器。我们基于大语言模型生成标签的提交方案在情感极性检测任务中获得了综合排名第一的成绩。我们的结果表明，基于大语言模型的标注方法在拉丁文本上展现出有前景的效果。