Given the number of Arabic speakers worldwide and the notably large amount of
content in the web today in some fields such as law, medicine, or even news,
documents of considerable length are produced regularly. Classifying those
documents using traditional learning models is often impractical since extended
length of the documents increases computational requirements to an
unsustainable level. Thus, it is necessary to customize these models
specifically for long textual documents. In this paper we propose two simple
but effective models to classify long length Arabic documents. We also
fine-tune two different models-namely, Longformer and RoBERT, for the same task
and compare their results to our models. Both of our models outperform the
Longformer and RoBERT in this task over two different datasets.

本文针对大量阿拉伯文文档分类问题，提出了两种简单而有效的机器学习模型，并对比了 RoBERT 和 Longformer 两个模型在不同数据集上的结果，结果显示我们的模型表现更优秀。

利用 BERT 语言模型进行阿拉伯语长文档分类

Leveraging BERT Language Model for Arabic Long Document Classification

In this chapter, we will first present the most standard computational
challenges met in Bayesian Statistics, focussing primarily on mixture
estimation and on model choice issues, and then relate these problems with
computational solutions. Of course, this chapter is only a terse introduction
to the problems and solutions related to Bayesian computations. For more
complete references, see Robert and Casella (2004, 2009), or Marin and Robert
(2007), among others. We also restrain from providing an introduction to
Bayesian Statistics per se and for comprehensive coverage, address the reader
to Robert (2007), (again) among others.

介绍了贝叶斯统计中的混合估计和模型选择问题，以及与之相关的计算解决方案，参考文献主要为 Robert 和 Casella (2004, 2009) 和 Marin 和 Robert (2007) 等。