Automatic readability assessment is relevant to building NLP applications for education, content analysis, and accessibility. However, Arabic readability assessment is a challenging task due to Arabic's morphological richness and limited readability resources. In this paper, we present a set of experimental results on Arabic readability assessment using a diverse range of approaches, from rule-based methods to Arabic pretrained language models. We report our results on a newly created corpus at different textual granularity levels (words and sentence fragments). Our results show that combining different techniques yields the best results, achieving an overall macro F1 score of 86.7 at the word level and 87.9 at the fragment level on a blind test set. We make our code, data, and pretrained models publicly available.

本文利用不同的方法从基于规则的方法到预训练的阿拉伯语言模型，针对阿拉伯语可读性评估提供了一系列实验结果。我们在不同的文本粒度级别（单词和句子片段）上报告了新创建的语料库上的结果。我们的结果表明，结合不同的技术可以取得最佳结果，在盲测集上的宏F1分数在单词级别为86.7，在片段级别为87.9。我们公开提供我们的代码、数据和预训练模型。

阿拉伯语可读性建模策略