Abstract Arabic Machine Translation (MT) has gained considerable attention from the
research community due to the widespread use of Arabic as one of the world's major …

While resources for English language are fairly sufficient to understand content on social
media, similar resources in Arabic are still immature. The main reason that the resources in …

In this paper, we study the performance of using ChatGPT as a machine translation tool for
translating English text to Arabic. For evaluating ChatGPT as a machine translator, it was …

Text preprocessing plays an important role in natural language processing (NLP) tasks
containing text classification, sentiment analysis, and machine translation. The …

As Arabic is considered a low-resource and a rich morphology language. As result, Arabic is
considered one of the most challenging languages in Machine Translation (MT). While …

Parallel corpora for low-resource Arabic dialects and English are limited and small-scale,
and most neural machine translation models, including Google Translate, rely mainly on …

Advances in neural machine translation utilizing pretrained language models (PLMs) have
shown promise in improving the translation quality between diverse languages. However …

Neural machine translation (NMT) has shown impressive performance when trained on
large-scale corpora. However, generic NMT systems have demonstrated poor performance …

This paper aims to investigate the challenges that translators face while post-editing
Machine Translation (MT) of idioms and expressions in literary works using Neural Machine …

This data paper introduces a comprehensive dataset tailored for word sense disambiguation
tasks, explicitly focusing on a hundred polysemous words frequently employed in Modern …