Name
Abstract | ||
|---|---|---|
In recent years, computer language area has witnessed important evolvement with applications in different domains. Machine Translation MT technology, considered as a subfield, has received important development with different approaches and techniques. Although, many MT systems and tools that support Arabic already exist; however, the quality of the translation is moderate and needs some improvement. In addition, the high demand for effective technologies to process and translate information from/to Arabic motivated the researchers in Arabic Machine Translation (AMT) to propose new approaches and solutions following the mainstream method, notably neural machine translation (NMT). In this paper, we provide a comprehensive review and compare different NMT approaches mainly for Arabic-English (and English-Arabic) machine translation research works. The discussed approaches address different linguistic and technical challenges and problems while demonstrating great success compared to traditional methods. The results of this work can serve the researchers and professional to be up-to-date and provide them with the necessary resources for modelling and improving of the AMT. These resources include corpora, toolkits, techniques and new models. The obtained results outline various findings, critics, and open issues in this area. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ACCESS.2021.3132488 | IEEE ACCESS |
Keywords | DocType | Volume |
Machine translation, Linguistics, Measurement, Tools, Task analysis, Syntactics, Morphology, Arabic machine translation, Google translation, BLEU, neural machine translation | Journal | 9 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jezia Zakraoui | 1 | 1 | 1.70 |
| Moutaz Saleh | 2 | 0 | 0.34 |
| Somaya Al-Maadeed | 3 | 0 | 0.34 |
| Jihad Mohamed Alja'am | 4 | 0 | 0.34 |