Problems of Machine Translation Systems in Arabic

Authors

  • May Al Shaikhli Amman Arab University

DOI:

https://doi.org/10.17507/jltr.1304.08

Keywords:

Machine Translation (MT), Human Translation (HT), source language, target language, translation approaches

Abstract

the human need for language translation has been increasing because of knowledge fields’ expansion and open communications across all countries throughout the world. Accordingly, the traditional translation has become insufficient and machine translation is the best alternative. However, despite its astounding development during the past decades, as an inevitable alternative, machine translation still faces many challenges that make it incomparable with human professional translation. This indicates that machine translation in all its types has to be supported by highly-developed tools that can enhance its effectiveness. This study showed the advantages of machine translation, discussed some of its most common challenges, and accordingly introduced some recommendations that should be taken into account to improve its effectiveness regarding Arabic Language.

Author Biography

May Al Shaikhli, Amman Arab University

Department of English Language and Translation

References

Adil, M. (2020). Exploring the role of translation in communicative language teaching or the communicative approach. SAGE Open, 10(2), 1-10. https://doi.org/10.1177/2158244020924403.

Alqudsi, A., Omar, N., & Shaker, K. (2014). Arabic machine translation: A survey. Artificial Intelligence Review, 42(4), 549–572. https://doi.org/10.1007/s10462-012-9351-1.

Alsohybe, N., Dahan, N., & Ba-Alwi, F. (2017). Machine-translation history and evolution: Survey for Arabic-English translations. Current Journal of Applied Science and Technology, 23(4), 1-19. https://doi.org/10.9734/cjast/2017/36124.

Ariana,Sim, M. A., & Pop, A. M. (2012). Managing problems when translating economic texts. Annals of the University of Oradea, Economic Science Series, 21(2), 152-157.

Elsayed, E. K., & Fathy, D. R. (2020). Sign language semantic translation system using ontology and deep learning. International Journal of Advanced Computer Science and Applications, 11(1), 141-147.https://doi.org/10.14569/ijacsa.2020.0110118.

Elsherif, H. M., & Soomro, T. R. (2017). Perspectives of Arabic machine translation. Journal of Engineering Science and Technology, 12(9) p .p 2315-2332.

Esplà-Gomis, M., Sánchez-Martínez, F., & Forcada, M. L. (2015). Using machine translation to provide target-language edit hints in computer aided translation based on translation memories. Journal of Artificial Intelligence Research, 53, 169-222. https://doi.org/10.1613/jair.4630.

Fan, H. (2021). Application of computer aided translation in technical English manual. Journal of Physics: Conference Series, 1961(1), 1-7. https://doi.org/10.1088/1742-6596/1961/1/012041.

Filmer, D. (2019). Voicing diversity? Negotiating Italian identity through voice-over translation in BBC broadcasting. Perspectives: Studies in Translation Theory and Practice, 27(2)., 299-315. https://doi.org/10.1080/0907676X.2018.1449871.

Freng, J., Ramabhadran, B., Hansen, J. H. L., & Williams, J. D. (2012). Trends in speech and language processing. IEEE Signal Processing Magazine, 29(1), 177-179. https://doi.org/10.1109/MSP.2011.943131.

Gaspari, F., & Hutchins, W. J. (2007). Online and free! Ten years of online machine translation: origins, developments, current use and future prospects. In Proceedings of Machine Translation Summit XI: Papers.‏September 10-14, 2007

Goetschalckx, J., Cucchiarini, C., & Hoorde, J. (2001). Machine Translation for Dutch: the NL-Translex Project - Why Machine Translation? European Commission. Translation Service. https://www.researchgate.net/publication/228958559_Machine_Translation_for_Dutch_the_NL-Translex_Project_Why_Machine_Translation.

Harrat, S., Meftouh, K., & Smaili, K. (2019). Machine translation for Arabic dialects (survey). Information Processing and Management, 56(2), 262-273. https://doi.org/10.1016/j.ipm.2017.08.003.

Hasabnis, N., & Sekar, R. (2016). Lifting assembly to intermediate representation: A novel approach leveraging compilers. http://seclab.cs.sunysb.edu/seclab/pubs/lisc.pdf.

He, S., Tu, Z., Wang, X., Wang, L., Lyu, M. R., & Shi, S. (2020). Towards Understanding Neural Machine Translation with Word Importance. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 953–962, Hong Kong, China. Association for Computational Linguistics. https://doi.org/10.18653/v1/d19-1088.

Imre, A. (2015). Translation problems of legal terms. https://www.researchgate.net/publication/287912230_Translation_problems_of_legal_terms

Kang, J. (2021). Automatic translation of spoken English based on improved machine learning algorithms. Journal of Ambient Intelligence and Humanized Computing, 4(3), 1-11. https://doi.org/10.1007/s12652-021-03198-6.

Karasaliu, A. (2016). Shaping swift’s expressiveness through the translation of his metaphors in albanian language. CBU International Conference Proceedings, 4, 325-330. https://doi.org/10.12955/cbup.v4.775.

Kim, J. W., Yoon, H., & Jung, H. Y. (2021). Linguistic-coupled age-to-age voice translation to improve speech recognition performance in real environments. IEEE Access, 9, 136476-136486. https://doi.org/10.1109/ACCESS.2021.3115608.

Koul, N., & Manvi, S. S. (2021). A proposed model for neural machine translation of Sanskrit into English. International Journal of Information Technology (Singapore), 13(1), 1-7. https://doi.org/10.1007/s41870-019-00340-8.

Lai, F., & Wan, Q. (2021). Discussion on the problems of computer assisted English translation. Journal Of Physics: Conference Series, 1992(3), 1-6. https://doi.org/10.1088/1742-6596/1992/3/032057.

Lindstromberg, S., & Eyckmans, J. (2020). The effect of frequency on learners’ ability to recall the forms of deliberately learned L2 multiword expressions. ITL - International Journal of Applied Linguistics, 171(1), 2–33. https://doi.org/10.1075/itl.18005.lin.

Luqman, H., & Mahmoud, S. A. (2020). A machine translation system from Arabic sign language to Arabic. Universal Access in the Information Society, 19(4), 892-904. https://doi.org/10.1007/s10209-019-00695-6.

Maruf, S., Saleh, F., & Haffari, G. (2021). A Survey on Document-level Neural Machine Translation: Methods and Evaluation. In ACM Computing Surveys, 54(2), 1-38.. https://doi.org/10.1145/3441691.

Omar, A., & Gomaa, Y. (2020). The machine translation of literature: Implications for translation pedagogy. International Journal of Emerging Technologies in Learning (IJET), 15(11), 228-235.‏ https://doi.org/10.3991/IJET.V15I11.13275.

Panayiotou, A., Gardner, A., Williams, S., Zucchi, E., Mascitti-Meuter, M., Goh, A. M. Y., You, E., Chong, T. W. H., Logiudice, D., Lin, X., Haralambous, B., & Batchelor, F. (2019). Language translation apps in health care settings: Expert opinion. JMIR MHealth and UHealth, 7(4), e11316. https://doi.org/10.2196/11316.

Prentice, F. M., & Kinden, C.E. (2018). Paraphrasing tools, language translation tools and plagiarism: An exploratory study. International Journal for Educational Integrity, 14 (11), 1-16. https://doi.org/10.1007/s40979-018-0036-7.

Rubino, R., & Sumita, E. (2020, December). Intermediate self-supervised learning for machine translation quality estimation. In Proceedings of the 28th International Conference on Computational Linguistics (pp. 4355-4360).‏

Schwartz, L. (2018). The history and promise of machine translation. In American Translators Association Scholarly Monograph Series (Vol. 18, pp. 161-190). John Benjamins Publishing Company. https://doi.org/10.1075/ata.18.08sch.

Soum-Paris, P. (2021). La Tour de Babel,’ 35 years later: Challenges and tools relating to the translation of archival terminology from English to French. Archives and Manuscripts, 49(1–2), 8-36. https://doi.org/10.1080/01576895.2020.1833226.

Sutopo, A., & Said, R. R. (2020). The influence of reading comprehension and vocabulary mastery toward translation skill. International Journal of Scientific and Technology Research, 9(1), p.p 1-8.

Tang, G., Müller, M., Rios, A., & Sennrich, R. (2020). Why self-attention? A targeted evaluation of neural machine translation architectures. In: Conference on Empirical Methods in Natural Language Processing (EMNLP), Brussels, 2 November 2018 - 4 November 2018. https://doi.org/10.18653/v1/d18-1458.

Xie, W., Feng, Y., Gu, S., & Yu, D. (2021). Importance-based neuron allocation for multilingual neural machine translation. https://doi.org/10.18653/v1/2021.acl-long.445.

Zaki, M. (2008). Arabic language and machine translation problems and solutions. The Eleventh Arabization Conference, Arab Educational, Cultural and Scientific Organization - Amman, 417–447.

Zhao, Z. (2021, February). Research on English translation skills and problems by using computer technology. In Journal of Physics: Conference Series (Vol. 1744, No. 4, p. 042111). IOP Publishing.‏ https://doi.org/10.1088/1742-6596/1744/4/042111.

Zheng, H. (2015). A case study of machine translation: Problems and suggestions. International Journal of English Linguistics, 5(2), 92–99. https://doi.org/10.5539/ijel.v5n2p92.

Zong, Z. (2018, September). Research on the relations between machine translation and human translation. In Journal of Physics: Conference Series (Vol. 1087, No. 6, p. 062046). IOP Publishing.‏ https://doi.org/10.1088/1742-6596/1087/6/062046.

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Published

2022-07-01

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