Views: 5,606
Arab World English Journal (AWEJ) Special Issue on ChatGPT, April 2024 Pp. 237-251
Department of English, College of Sciences and Humanities
Prince Sattam Bin AbdulAziz University, Wadi ad-Dawasir, Saudi Arabia
This study examines the advancements in AI-driven machine translation, specifically focusing on the accurate translation of Arabic colloquial expressions. It aims to assess the progress made by Large Language Models, such as Bing AI Chat, compared to traditional machine translation systems. By focusing on colloquial expressions, this research aims to shed light on the challenges and opportunities for improvement in machine translation systems, particularly when dealing with the complexities of translating informal Arabic utterances. Building upon At-tall’s 2019 thesis, which compared Google Translate and human translators, the study employs the same Arabic sentences as a test dataset, allowing for a direct comparison between 2019 translations and those produced by current machine translation tools. The findings indicate limited improvement in Google Translate since 2019, with Bing Translator exhibiting a similar level of translation accuracy. In contrast, Bing AI Chat consistently outperformed the other systems, showcasing the potential of Large Language Model machine translation. Notably, Bing AI Chat provided interpretations and valuable comments on the tested Arabic phrases, demonstrating a deeper understanding of the intended meaning. This study contributes significantly to the field of machine translation by providing evidence of the potential of Large Language Model systems in producing more accurate Arabic-English translations. It emphasizes the advantage of Large Language Models in dealing with non-standard Arabic expressions, encouraging further exploration of Large Language Model-powered approaches in machine translation. The findings offer a promising pathway towards achieving more accurate and expressive translations across diverse languages and cultures.
Aldawsari, H. A. (2024). Evaluating Translation Tools: Google Translate, Bing Translator, and Bing AI on Arabic Colloquialisms. Arab World English Journal (AWEJ) Special Issue on ChatGPT, April 2024: 237-251.
Al-Btoush, M. (2014). English Loanwords in Colloquial Jordanian Arabic. International Journal of Linguistics, 6 (2), 109-119. https://doi.org/10.5296/ijl.v6i2.5086
Aldawsari, H. A. H. (2023). Comparing the Performance of Google Translate and SYSTRAN on Arabic Lexical Ambiguity. Arab World English Journal for Translation & Literary Studies, 7(3) 19- 34. http://dx.doi.org/10.24093/awejtls/vol7no3.2
Ali, M. A. (2020). Quality and machine translation: An evaluation of online machine translation of English into Arabic texts. Open Journal of Modern Linguistics, 10(05), 524–548. https://doi.org/10.4236/ojml.2020.105030
Al-Jarf, R. (2023). Pluralization of social media loan terminology in colloquial Arabic. British Journal of Applied Linguistics, 3(2), 60-68. https://doi.org/10.32996/bjal.2023.3.2.6
Al-Kharabsheh, A., & Yassin, O. (2017). Translation of colloquialisms in the Arabic-into-English subtitled film, The Dups. International Journal of Comparative Literature and Translation Studies, 5(3), 18. https://doi.org/10.7575/aiac.ijclts.v.5n.3p.18
Almahasees, Z. (2021). Analysing English-Arabic Machine Translation: Google Translate, Microsoft Translator, and Sakhr. Routledge. https://doi.org/10.4324/9781003191018
Alnamer, A., & Alnamer, S. (2018). The Use of Loanwords in Emirati Arabic According to Speakers’ Gender, Educational Level, and Age. International Journal of Applied Linguistics & English Literature, 7(4), 158- 176. https://doi.org/10.7575/aiac.ijalel.v.7n.3p.158
Al-Saidat, E. (2011). English Loanwords in Jordanian Arabic: Gender and Number Assignment. Language Forum, 37(1), 59-72
Ameur, M. S. H., Meziane, F., & Guessoum, A. (2020). Arabic machine translation: A survey of the latest trends and challenges. Computer Science Review, 38, 100305. https://doi.org/10.1016/j.cosrev.2020.100305
At-tall, S. M. (2019). Comparative study between Google Translator and human translator in rendering colloquial Arabic expressions in the late Prime Minister Wasfi At-Tall’s speeches into English, (Unpublished Master’s thesis). Yarmouk University, Irbid, Jordan.
Banimelhem, O., & Amayreh, W. (2023). Is ChatGPT a Good English to Arabic Machine Translation Tool?. In 2023 14th International Conference on Information and Communication Systems (ICICS) (pp. 1-6). IEEE. https://doi.org/10.1109/ICICS60529.2023.10330525.
Boughorbel, S., & Hawasly, M. (2023). Analyzing Multilingual Competency of LLMs in Multi-Turn Instruction Following: A Case Study of Arabic. SIGARAB Arabic NLP. https://doi.org/10.48550/arXiv.2310.14819
Habash, N., & Diab, M. T. (2012). Arabic natural language processing: Why it matters and how to get started. In Proceedings of the ACL-HLT 2012 Workshop on Computational Approaches to Arabic Script Languages (pp. 1-9). Association for Computational Linguistics.
Harrat, S., Meftouh, K., & Smaili, K. (2019). Machine translation for Arabic dialects (survey). Information Processing & Management, 56(2), 262-273. https://doi.org/10.1016/j.ipm.2017.08.003.
Huang, H., Wu, S., Liang, X., Wang, B., & Zhao, T. (2023). Towards Making the Most of LLM for Translation Quality Estimation. In CCF International Conference on Natural Language Processing and Chinese Computing (pp. 375-386). Cham: Springer Nature Switzerland. https://doi.org/10.3115/1073083.1073135
Hutchins, W. J. (2001). Early Years in Machine Translation. John Benjamins Publishing Company. https://doi.org/10.1075/sihols.97
Hutchins, W., & Somers, H. (1992). An introduction to machine translation. London: Academic Press.
Rinsum, H. (2023). CHATGPT, Bing Chat or bard? Agrarzeitung, 78(39), 10–10. https://doi.org/10.51202/1869-9707-2023-39-010
Jibreel, I. (2023). Online machine translation efficiency in translating fixed expressions between English and Arabic (proverbs as a case-in-point). Theory and Practice in Language Studies, 13(5), 1148–1158. https://doi.org/10.17507/tpls.1305.07
Joyce, M. (2008). Anglo-American support for Jordan: The career of King Hussein. New York: Palgrave Macmillan. https://doi.org/10.1057/9780230611641
Koehn, P. (2010). Statistical machine translation. Cambridge, UK: Cambridge University Press. https://doi.org/10.1017/CBO9780511815829
Lee, T. (2023). Artificial intelligence and posthumanist translation: ChatGPT versus the translator. Applied Linguistics Review, 14(4), 60-66. https://doi.org/10.1515/applirev-2023-0122
Morris, B. (2001). Righteous victims: A history of the Zionist-Arab conflict, 1881-2001. New York: Vintage https://doi 10.7440/histcrit21.2001.12
Okpor, M. D. (2014). Machine translation approaches: Issues and challenges. International Journal of Computer Science Issues (IJCSI), 11(5), 159. https://doi.org/10.20943/01201702.5457
Susser, A. (1994). On both banks of the Jordan: A political biography of Wasfi al-Tall. London: Frank Cass
Zakraoui, J., Saleh, M., Al-Maadeed, S., & Alja’am, J. M. (2021). Arabic Machine Translation: A Survey with Challenges and Future Directions. IEEE Access, 9, 161445-161468. https://doi.org/10.1109/ACCESS.2021.3132488
Hamad Abdullah H Aldawsari is an Assistant Professor of Translation Studies at the College of Sciences and Humanities, Prince Sattam bin Abdulaziz University (PSAU), Wadi Addawasir, Saudi Arabia. He completed his MA and PhD at the University of Birmingham, UK. His research interests include Translator Studies & Linguistics.
ORCID ID https://orcid.org/0009-0001-2041-9760.