Abstract
This paper describes submissions from the team Nostra Domina to the EvaLatin 2024 shared task of emotion polarity detection. Given the low-resource environment of Latin and the complexity of sentiment in rhetorical genres like poetry, we augmented the available data through automatic polarity annotation. We present two methods for doing so on the basis of the $k$-means algorithm, and we employ a variety of Latin large language models (LLMs) in a neural architecture to better capture the underlying contextual sentiment representations. Our best approach achieved the second highest macro-averaged Macro-$F_1$ score on the shared task's test set.
Abstract (translated)
本文描述了来自Nostra Domina团队在EvaLatin 2024共同任务情感极性检测中的提交。考虑到拉丁语的低资源环境和诗歌等修辞体中情感的复杂性，我们通过自动极性注释来增加可用数据。我们在基于$k$-means算法的两种方法上进行研究，并使用各种拉丁大型语言模型（LLMs）来构建神经架构，更好地捕捉潜在上下文情感表示。我们最好的方法在共享任务的测试集中获得了第二个最高的宏观平均Macro-$F_1$得分。
URL
https://arxiv.org/abs/2404.07792
