Survey of Arabic Machine Translation, Methodologies, Progress, and Challenges

Abstract

For the longest time, translation was a labor-intensive process that required just human effort. While human translation remains the most reliable method of textual content translation, it takes longer and is more expensive if done for each individual piece of information. Several Machine Translation (MT) approaches have recently emerged to facilitate the migration of any content across languages, especially for the low resource languages such as the Arabic Language. Given that Arabic is one of the world's most widely spoken languages, the task of Arabic machine translation has recently gotten a lot of interest from the scientific community. Indeed, the amount of study devoted to these low resources languages has resulted in some significant accomplishments; however, the status of Arabic MT systems falls short of the quality obtained for other languages. As a result, this survey examines the origins and main development timeline of MT approaches, investigates the significant branches, and categorizes different study orientations. In addition, it gives a comprehensive overview of the key research works that have been completed in the field of Arabic Neural MT (ANMT) and discusses possible future research prospects in this discipline.