An Automatic Modern Standard Arabic Text Simplification System: A Corpus-Based Approach

This thesis brings together an overview of Text Readability (TR) about Text Simplification (TS) with an application of both to Modern Standard Arabic (MSA). It will present our findings on using automatic TR and TS tools to teach MSA, along with challenges, limitations, and recommendations about enhancing the TR and TS models. Reading is one of the most vital tasks that provide language input for communication and comprehension skills. It is proved that the use of long sentences, connected sentences, embedded phrases, passive voices, non- standard word orders, and infrequent words can increase the text difficulty for people with low literacy levels, as well as second language learners. The thesis compares the use of sentence embeddings of different types (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. The accuracy of the 3-way CEFR (The Common European Framework of Reference for Languages Proficiency Levels) classification is F-1 of 0.80 and 0.75 for Arabic-Bert and XLM-R classification, respectively and 0.71 Spearman correlation for the regression task. At the same time, the binary difficulty classifier reaches F-1 0.94 and F-1 0.98 for the sentence-pair semantic similarity classifier. TS is an NLP task aiming to reduce the linguistic complexity of the text while maintaining its meaning and original information (Siddharthan, 2002; Camacho Collados, 2013; Saggion, 2017). The simplification study experimented using two approaches: (i) a classification approach and (ii) a generative approach. It then evaluated the effectiveness of these methods using the BERTScore (Zhang et al., 2020) evaluation metric. The simple sentences produced by the mT5 model achieved P 0.72, R 0.68 and F-1 0.70 via BERTScore while combining Arabic- BERT and fastText achieved P 0.97, R 0.97 and F-1 0.97. To reiterate, this research demonstrated the effectiveness of the implementation of a corpus-based method combined with extracting extensive linguistic features via the latest NLP techniques. It provided insights which can be of use in various Arabic corpus studies and NLP tasks such as translation for educational purposes

A dataset for the evaluation of lexical simplification

Lexical Simplification is the task of replacing individual words of a text with words that are easier to understand, so that the text as a whole becomes easier to comprehend, e.g. by people with learning disabilities or by children who learn to read. Although this seems like a straightforward task, evaluating algorithms for this task is not so. The problem is how to build a dataset that provides an exhaustive list of easier to understand words in different contexts, and to obtain an absolute ordering on this list of synonymous expressions. In this paper we reuse existing resources for a similar problem, that of Lexical Substitution, and transform this dataset into a dataset for Lexical Simplification. This new dataset contains 430 sentences, with in each sentence one word marked. For that word, a list of words that can replace it, sorted by their difficulty, is provided. The paper reports on how this dataset was created based on the annotations of different persons, and their agreement. In addition we provide several metrics for computing the similarity between ranked lexical substitutions, which are used to assess the value of the different annotations, but which can also be used to compare the lexical simplifications suggested by an algorithm with the ground truth model.status: publishe