Development of Machine Translation Models: A Systematic Review

  • Afrah Almansoori, Saeed Al Mansoori, Mohammed Alshamsi, Said A. Salloum, Khaled Shaalan

Abstract

Advanced Natural Language Processing (ANLP) has a wide variety of domains, including machine translation (MT) and its numerous models. It specifically focuses on two models of MT, which are the Statistical Machine Translation (SMT) model and Neural Machine Translation (NMT) model. SMT operates by using a database of existing information and the probability distribution of the original and target languages in order to perform translation. By contrast, NMT uses deep and representative learning in order to perform the same task. Some of the major challenges faced by machine translation technologies are posed by the dynamic fluidity of human language and the major contrast among different languages.  Due to this, the MT industry experienced a major development since its establishment. This research paper aims to explore in-depth the field of MT and find out the latest developments in this area as well as to compare and contrast the different translation models: namely, SMT and NMT. A systematic literature review has been conducted in order to find highly reputed peer-reviewed papers investigating the same topic. A set of research questions has been developed and their rationale has been explained. The research strategy used to conduct this study is based on a thorough search on academic databases using keywords derived from the research questions. Finally, pre-selection and selection criteria have been examined and cross-referenced in order to find the most important pieces of literature. Based on the literature review, the research questions have been addressed. Besides, we highlighted MT methods, which aim to improve the quality of the translations that they produce.

Published
2020-06-01
How to Cite
Afrah Almansoori, Saeed Al Mansoori, Mohammed Alshamsi, Said A. Salloum, Khaled Shaalan. (2020). Development of Machine Translation Models: A Systematic Review. International Journal of Control and Automation, 13(02), 1462 - 1483. Retrieved from https://sersc.org/journals/index.php/IJCA/article/view/32923
Section
Articles