Translating English Number Idioms into Arabic: A Neural Machine Translation Evaluation

Abdullah Sanad Mohammad Aldela'a, Mai Abdel karim Mohammad Malkawi and Ayman Altalahin
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Abdullah Sanad Mohammad Aldela'a: The Language Center, The Hashemite University, Zarqa, Jordan
Mai Abdel karim Mohammad Malkawi: The Language Center, The Hashemite University, Zarqa, Jordan
Ayman Altalahin: Amman Arab University, Amman, Jordan

Technium Social Sciences Journal, 2025, vol. 78, issue 1, 312-318

Abstract: Idiomatic expressions, particularly those involving numbers, pose significant challenges for neural machine translation (NMT) systems due to their non-literal meanings and cultural specificity. This study investigates the efficacy of NMT systems, specifically Google Translate and Chatgpt, in translating English number idioms into Arabic. A corpus of 30 English number idioms was investigated and their translations analyzed in terms of literalness, cultural appropriateness, and semantic accuracy. The findings reveal that while some idioms are accurately translated with functional equivalents, a substantial number are rendered literally. This leads to some problems in accuracy culturally and meaningfully. The study underscores the need for enhancing NMT systems with idiom-specific modules and culturally enriched datasets to improve translation quality.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:tec:journl:ojs:13330

DOI: 10.47577/tssj.v78i1.13330

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