Abstract
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.
Abstract (translated)
本文是TartuNLP团队在2024年EvaLatin共享任务中提交的关于情感极性检测历史拉丁文本的任务。我们的系统依赖于两种不同的数据注释方法：1）采用主办方提供的极性词典创建基于策略的标签；2）使用GPT4生成标签。我们使用适应器框架进行参数高效的微调，并尝试了为训练语言和任务适配器进行本体和跨语言知识传递。使用LLM生成的标签，我们在情感极性检测任务中获得了 overall first place 的成绩。我们的结果表明，基于LLM的注释在拉丁文本上显示出有希望的结果。
URL
https://arxiv.org/abs/2405.01159
