Evaluation of Arabic to English Machine Translation Systems

Abstract

Arabic machine translation has an important role in most NLP tasks. Many machine translation systems that support Arabic exist already; however the quality of the translation needs to be improved. In this paper, we review different research approaches for Arabic-to-English machine translation. The approaches use various evaluation methods, datasets, and tools to measure their performance. Moreover, this paper sheds light on several methods and assessment efforts, and future ideas to improve the machine translation quality of Arabic-to-English. The review results depict three major findings; first neural machine translation approaches outperform other approaches in many aspects. Second, the recently emerging attention-based approach is being useful to improve the performance of neural machine translation for all languages. Third, the translation performance quality depends on the quality of the dataset, well-behaved aligned corpus, and the evaluation technique used.