In this paper, we focus on the detection of semantic changes in Slovene, a
less resourced Slavic language with two million speakers. Detecting and
tracking semantic changes provides insights into the evolution of the language
caused by changes in society and culture. Recently, several systems have been
proposed to aid in this study, but all depend on manually annotated gold
standard datasets for evaluation. In this paper, we present the first Slovene
dataset for evaluating semantic change detection systems, which contains
aggregated semantic change scores for 104 target words obtained from more than
3000 manually annotated sentence pairs. We evaluate several existing semantic
change detection methods on this dataset and also propose a novel approach
based on optimal transport that improves on the existing state-of-the-art
systems with an error reduction rate of 22.8%.

我们研究斯洛文尼亚语（斯洛维尼亚语），这是一种资源较少的斯拉夫语言，探测语义变化，通过对社会和文化的变化，研究揭示语言的演变。我们提出了第一个斯洛文尼亚语数据集，用于评估语义变化检测系统，该数据集包含 104 个目标词的聚合语义变化分数，这些分数是从超过 3000 个手动注释的句子对中获得的。我们在该数据集上评估了几种现有的语义变化检测方法，并提出了一种基于最优输运的新方法，相比现有最先进系统，减少了 22.8% 的错误率。