This paper will present textual corpora for Serbian (and Serbo-Croatian),
usable for the training of large language models and publicly available at one
of the several notable online repositories. Each corpus will be classified
using multiple methods and its characteristics will be detailed. Additionally,
the paper will introduce three new corpora: a new umbrella web corpus of
Serbo-Croatian, a new high-quality corpus based on the doctoral dissertations
stored within National Repository of Doctoral Dissertations from all
Universities in Serbia, and a parallel corpus of abstract translation from the
same source. The uniqueness of both old and new corpora will be accessed via
frequency-based stylometric methods, and the results will be briefly discussed.

该文章介绍了用于大型语言模型训练的塞尔维亚语（和塞尔维亚 - 克罗地亚语）的文本语料库，并公开存储在多个知名在线资源库之一。对每个语料库使用多种方法进行分类，并详细说明其特点。此外，文章还介绍了三个新的语料库：一个新的塞尔维亚 - 克罗地亚语综合网络语料库，一个基于塞尔维亚所有大学国家博士论文库中的博士学位论文的高质量语料库，以及同一来源的摘要翻译平行语料库。将通过基于频率的文体测量方法评估新旧语料库的独特性，并对结果进行简要讨论。

塞尔维亚语言建模的新文本语料库

New Textual Corpora for Serbian Language Modeling

In this paper, we focus on generating a synthetic question answering (QA)
dataset using an adapted Translate-Align-Retrieve method. Using this method, we
created the largest Serbian QA dataset of more than 87K samples, which we name
SQuAD-sr. To acknowledge the script duality in Serbian, we generated both
Cyrillic and Latin versions of the dataset. We investigate the dataset quality
and use it to fine-tune several pre-trained QA models. Best results were
obtained by fine-tuning the BERTi\'c model on our Latin SQuAD-sr dataset,
achieving 73.91% Exact Match and 82.97% F1 score on the benchmark XQuAD
dataset, which we translated into Serbian for the purpose of evaluation. The
results show that our model exceeds zero-shot baselines, but fails to go beyond
human performance. We note the advantage of using a monolingual pre-trained
model over multilingual, as well as the performance increase gained by using
Latin over Cyrillic. By performing additional analysis, we show that questions
about numeric values or dates are more likely to be answered correctly than
other types of questions. Finally, we conclude that SQuAD-sr is of sufficient
quality for fine-tuning a Serbian QA model, in the absence of a manually
crafted and annotated dataset.

使用改进的翻译 - 对齐 - 检索方法生成了最大的塞尔维亚问答数据集 SQuAD-sr，并使用该数据集对几个预训练的问答模型进行了微调，最佳结果表明我们的模型超过了零 - shot 基线但未超越人类表现。

针对塞尔维亚语问答的 Transformer 模型的合成数据集创建和微调

Synthetic Dataset Creation and Fine-Tuning of Transformer Models for  Question Answering in Serbian

This paper presents the results of the first application of BERTopic, a
state-of-the-art topic modeling technique, to short text written in a
morphologi-cally rich language. We applied BERTopic with three multilingual
embed-ding models on two levels of text preprocessing (partial and full) to
evalu-ate its performance on partially preprocessed short text in Serbian. We
also compared it to LDA and NMF on fully preprocessed text. The experiments
were conducted on a dataset of tweets expressing hesitancy toward COVID-19
vaccination. Our results show that with adequate parameter setting, BERTopic
can yield informative topics even when applied to partially pre-processed short
text. When the same parameters are applied in both prepro-cessing scenarios,
the performance drop on partially preprocessed text is minimal. Compared to LDA
and NMF, judging by the keywords, BERTopic offers more informative topics and
gives novel insights when the number of topics is not limited. The findings of
this paper can be significant for re-searchers working with other
morphologically rich low-resource languages and short text.

该论文采用 BERTopic 主题建模技术对塞尔维亚语的短文本进行了首次应用，结果表明在部分预处理的情况下，BERTopic 可以提供信息丰富的主题。与 LDA 和 NMF 相比，BERTopic 提供了更多有信息量的主题，并在主题数不受限制时提供新的见解。这些研究结果对于处理其他形态丰富的低资源语言和短文本的研究者具有重要意义。