Human-Computer Interaction: Method of Translation Facebook Auto Translation in Translating Arabic–Indonesian on Al Jazeera Channel Posts

Authors

  • Siti Julfiah Student of Master of Linguistics Program, Faculty of Cultural Sciences, Hasanuddin University, Makassar
  • Muhammad Hasyim Lecturer Team for Master of Linguistics Program, Faculty of Cultural Sciences, Hasanuddin University, Makassar
  • Andi Agussalim Lecturer Team for Master of Linguistics Program, Faculty of Cultural Sciences, Hasanuddin University, Makassar

Keywords:

Facebook Auto Translation, translation, Al Jazeera Channel, Arabic, machine translation

Abstract

The main problem of translation lies in the method used by the translator. Newmark suggests in theory eight translation methods commonly used by "human" translators in translating texts. However, whether this theory also applies to machine translators is a question that this study attempts to answer. More specifically, this study aims to analyze the translation method used by Facebook Auto Translation (FAT) in translating Arabic-Indonesian based on Newmark's V diagram theory. This research is a combination of quantitative and qualitative research. Data mapping was carried out quantitatively, then data analysis was carried out qualitatively, in which the collection, analysis and presentation of data was carried out descriptively.The results showed that by the 58 posts and their translated results analyzed, the researchers found that FAT only applied two of the eight methods proposed by Newmark. The two methods are word-for-word translation and literal translation. A total of 6 translation results (10.34%) are classified as word-for-word translations and 52 translation results (89.66%) are classified as literal translations. The translation method applied by FAT is dominated by literal translation because basically Arabic grammar and Indonesian grammar are different. Arabic has a predicate + subject sentence pattern, although there are also some that have a subject + predicate pattern. Besides that, Arabic also has additional letters or words to verbs which do not have to be translated into Indonesian but must still be in the text because their role is very important in determining meaning. The difference in grammatical structure causes the order of translation to also change so that FAT applies more literal translation methods.

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References

Abbas, Asriani, Kaharuddin, Hasyim, Muhammad. 2022. The Organization of Personal Pronouns in Sentence Structure Construction of Makassarese Language. Journal of Language Teaching and Research, 13, (1), pp. 161-171.

Al-Laith, Ali and Muhammad Shahbaz. 2021. Tracking sentiment towards news entities from Arabic news on social media. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2021.01.015

Ameur, Mohamed Seghir Hadj, et al. 2020. Arabic Machine Translation: A survey of the latest trends and challenges. Computer Science Reviews. https://doi.org/10.1016/j.cosrev.2020.100305

Arafah, Burhanuddin and Muhammad Hasyim. 2019. The Language of Emoji in Social Media in The Second Annual International Conference on Language and Literature. KnE Social Sciences. https://doi.org/10.18502/kss.v3i19.4880

Berrichi, Safae and Azzeddine Mazroui. 2021. Addressing Limited Vocabulary and Long Sentences Constraints in English-Arabic Neural Machine Translation. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-020-05328-2

Brour, Mourad and Abderrahim Benabbou. 2021. ATLASLang NMT: Arabic text language into Arabic sign language neural machine translation. Journal of King Saud University – Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2019.07.006

Faisal, A., Hasyim, M. 2022. Warkop (Coffeehouse) And The Construction Of Public Space In Makassar City. International Journal of Professional Business Review, 7(5), 1-19

Farkas, Anna and Renáta Németh. 2022. How to measure gender bias in machine translation: Real-world oriented machine translators, multiple reference points. Social Sciences & Humanities Open. https://doi.org/10.1016/j.ssaho.2021.100239.

Hasyim, M., Saleh, F., Yusuf, R., Abbas, A. 2021. Artificial Intelligence: Machine Translation Accuracy in Translating French-Indonesian Culinary Texts. International Journal of Advanced Computer Science and Applications, 2021, 12(3), 186-191

Karamalak, Olga Alexeevna and Elena Valerevna Pozhidaeva. 2019. Linguistic and cultural aspects of relationship status on Facebook. Heliyon. https://doi.org/10.1016/j.heliyon.2019.e02878.

Maknun, T., Hasjim, M., Muslimat, M., Hasyim, M. 2020. The form of the traditional bamboo house in the Makassar culture: A cultural semiotic study. Semiotica, 2020 (235)

Munkova, Dasa, et al. 2020. Evaluation of Machine Translation Quality through the Metrics of Error Rate and Accuracy. Procedia Computer Science. https://doi.org/10.1016/j.procs.2020.04.142

Newmark, Peter. 1988. A Textbook of Translation. New York: Prentice Hall.

Rajesh, Thipparupu, B. Rangaiah. 2022. Relationship between personality traits and facebook addiction: A meta-analysis. Heliyon. https://doi.org/10.1016/j.heliyon.2022.e10315

Shalash, Dana. 2020. Affiliative Other Initiative Repeats (AOIRs) in Arabic broadcast news interviews: The case of Aljazeera's “The Opposite Direction”. Journal of Pragmatics.https://doi.org/10.1016/j.pragma.2020.08.009

Solyman, Aiman, et al. 2021. Synthetic data with neural machine translation for automatic correction in Arabic grammar. Egyptian Informatics Journal. https://doi.org/10.1016/j.eij.2020.12.001

Tan, Zhixing, et al. 2020. Neural machine translation: A review of methods, resources, and tools. AI Open. https://doi.org/10.1016/j.aiopen.2020.11.001

Vaisman, Carmel L., et al. 2018. Nonhuman language agents in online collaborative communities: Comparing Hebrew Wikipedia and Facebook translations. Discourse, Context & Media. https://doi.org/10.1016/j.dcm.2017.10.002

Wang, Haifeng, et al. 2021. Progress in Machine Translation. Engineering. https://doi.org/10.1016/j.enwg.2021.03.02330

Zwischenberger, Cornelia. 2022. Online collaborative translation: its ethical, social, and conceptual conditions and consequences. Perspectives. https://doi.org/10.1080/0907676X.2021.1872662

Translation method implemented by FAT.

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Published

28.02.2023

How to Cite

Siti Julfiah, Muhammad Hasyim, & Andi Agussalim. (2023). Human-Computer Interaction: Method of Translation Facebook Auto Translation in Translating Arabic–Indonesian on Al Jazeera Channel Posts. International Journal of Intelligent Systems and Applications in Engineering, 11(4s), 527–535. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2723

Issue

Vol. 11 No. 4s (2023)

Section

Research Article

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