A Transformer Seq2Seq Model with Fast Fourier Transform Layers for Rephrasing and Simplifying Complex Arabic Text

Abstract

Text simplification is a fundamental unsolved problem for Natural Language Understanding (NLU) models, which is deemed a hard-to-solve task. Recently, this hard task has aimed to simplify texts with complex linguistic structures and improve their readability, not only for human readers but also for boosting the performance of many natural language processing (NLP) applications. Towards tackling this hard task for the low-resource Arabic NLP, this paper presents a text split-and-rephrase strategy for simplifying complex texts, which depends principally on a sequence-to-sequence Transformer-based architecture (which we call TSimAr). For evaluation, we created a new benchmarking corpus for Arabic text simplification (so-called ATSC) containing 500 articles besides their corresponding simplifications. Through our automatic and manual analyses, experimental results report that our TSimAr evidently outperforms all the publicly accessible state-of-the-art text-to-text generation models for the Arabic language as it achieved the best score on SARI, BLEU, and METEOR metrics of about 0.73, 0.65, and 0.68, respectively.