Arabic machine translation

Arabic is one of the major languages that have been given attention by machine translation (MT) researchers since the very early days of MT and specifically in the U.S. The language has always been considered "due to its morphological, syntactic, phonetic and phonological properties [to be] one of the most difficult languages for written and spoken language processing."[1] Arabic "differs tremendously in terms of its characters, morphology and diacritization from other languages."[1] Accordingly, researchers cannot always import solutions from other languages, and today Arabic machine translation still needs more efforts to be improved, mainly in the area of semantic representation systems, which are essential for achieving high-quality translation.

In 2022, Abu Dhabi's Technology Innovation Institute (TII) unveiled 'Noor,' the world's largest natural language processing model for Arabic language translation. Prior to this, the largest Arabic model was AraGPT, a model trained on 1.5 billion parameters. TII trained Noor on 10 billion parameters.

## Contents

* 1 Approaches for the study of machine processing of Arabic
* 1.1 Particularistic approaches
* 1.2 Universalist approaches
* 2 References
* 3 External links

## Approaches for the study of machine processing of Arabic

### Particularistic approaches

Particularistic approaches describe the linguistic features of Arabic and use them for a local processing approach specific to the internal linguistic system of Arabic. They are concerned with the morphological and semantic aspects of Arabic. Sakhr is one of the Arabic-speaking groups developing systematically machine processing of Arabic.[2]

### Universalist approaches

Universalist approaches use the methods and systems proved to be useful in other languages like English or French making some adaptations if necessary. The focus here is on the syntactic aspects of the linguistic system in general. This approach is followed by most of the companies producing software applications for Arabic.

## References

1. ↑ 1.0 1.1 Zughoul, Muhammad; Abu-Alshaar, Awatef (3 August 2005). "English/Arabic/English Machine Translation: A Historical Perspective". Translators' Journal 50 (3): 1022–1041. http://id.erudit.org/iderudit/011612ar. Retrieved 2 June 2011.
2. ↑ "Arabic Machine Translation". Archived from the original on 15 July 2011. https://web.archive.org/web/20110715230846/http://international.sakhr.com/arabic-machine-translation-MT.html. Retrieved 4 June 2011.

## External links

* Salem, Yasser (March 2009). "UNIARAB: An universal machine translator system for Arabic Based on Role and Reference Grammar". Proceedings of the 31st Annual Meeting of … (Academia.edu). https://ucd-ie.academia.edu/YasserSalem/Papers/527105/UNIARAB_An_universal_machine_translator_system_for_Arabic_Based_on_Role_and_Reference_Grammar. Retrieved 19 June 2011.
* Salem, Yasser (March 2009). "UniArab: An RRG Arabic-to-English machine translation software". Proceedings of the Role and Reference … (in Proceedings of the 2009 International Conference on Role and Reference Grammar, University of California, Berkeley, USA). https://www.academia.edu/530233. Retrieved 24 March 2014.
* Salem, Yasser (April 2009). MSc Thesis: A generic framework for Arabic to English machine translation of simplex sentences using the Role and Reference Grammar linguistic model. Academia.edu. https://www.academia.edu/530244. Retrieved 24 March 2014.
* Can AI Machine Translation Tools Replace Human Translators?

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