This paper presents an attempt to build a Modern Standard Arabic (MSA) sentence-level simplification system. We experimented with sentence simplification using two approaches: (i) a classification approach leading to lexical simplification pipelines which use Arabic-BERT, a pre-trained contextualised model, as well as a model of fastText word embeddings; and (ii) a generative approach, a Seq2Seq technique by applying a multilingual Text-to-Text Transfer Transformer mT5. We developed our training corpus by aligning the original and simplified sentences from the internationally acclaimed Arabic novel Saaq al-Bambuu. We evaluate effectiveness of these methods by comparing the generated simple sentences to the target simple sentences using the BERTScore evaluation metric. The simple sentences produced by the mT5 model achieve P 0.72, R 0.68 and F-1 0.70 via BERTScore, while, combining Arabic-BERT and fastText achieves P 0.97, R 0.97 and F-1 0.97. In addition, we report a manual error analysis for these experiments.

This paper presents (AraSAS) the first open-source Arabic semantic analysis tagging system. AraSAS is a software framework that provides full semantic tagging of text written in Arabic. AraSAS is based on the UCREL Semantic Analysis System (USAS) which was first developed to semantically tag English text. Similarly to USAS, AraSAS uses a hierarchical semantic tag set that contains 21 major discourse fields and 232 fine-grained semantic field tags. The paper describes the creation, validation and evaluation of AraSAS. In addition, we demonstrate a first case study to illustrate the affordances of applying USAS and AraSAS semantic taggers on the Zayed University Arabic-English Bilingual Undergraduate Corpus (ZAEBUC) (Palfreyman and Habash, 2022), where we show and compare the coverage of the two semantic taggers through running them on Arabic and English essays on different topics. The analysis expands to compare the taggers when run on texts in Arabic and English written by the same writer and texts written by male and by female students. Variables for comparison include frequency of use of particular semantic sub-domains, as well as the diversity of semantic elements within a text.

In this paper, we present a Modern Standard Arabic (MSA) Sentence difficulty classifier, which predicts the difficulty of sentences for language learners using either the CEFR proficiency levels or the binary classification as simple or complex. We compare the use of sentence embeddings of different kinds (fastText, mBERT , XLM-R and Arabic-BERT), as well as traditional language features such as POS tags, dependency trees, readability scores and frequency lists for language learners. Our best results have been achieved using fined-tuned Arabic-BERT. The accuracy of our 3-way CEFR classification is F-1 of 0.80 and 0.75 for Arabic-Bert and XLM-R classification respectively and 0.71 Spearman correlation for regression. Our binary difficulty classifier reaches F-1 0.94 and F-1 0.98 for sentence-pair semantic similarity classifier.