Journal of Languages and Translation
Volume 5, Numéro 2, Pages 271-286
2025-07-01

Translating “inside The Box”: A Paradigm Shift To Neural Machine Translation



Authors : Aloui Asma .

Abstract

The present paper sheds light on two prominent yet divergent approaches in the realm of machine translation; i.e., statistical machine translation (SMT) and neural machine translation (NMT). The paper focuses on neural machine translation and aims at providing a critical analysis of machine translation output as opposed to that of a human reference by probing the translation strategies deployed by three online translation tools; Google Translate, Yandex Translate, and Reverso Translation. It is worth mentioning that the aforementioned online translation tools, unlike other ones, support Arabic in their language database, which caters to the needs of translation agents seeking language transfer involving Arabic as a source and/or target language. In order to examine the efficiency of these translation tools in terms of specialized translation, the corpus that has been chosen includes passages from three different text genres; political, economic, and judicial. The findings suggest that online translation tools prove to be efficient in translating sentences with short and simple structures with minimal interpretation scope. The findings also suggest that online translation tools help perform corpus-based translation tasks. Nevertheless, human judgment and supervision as well as editing are required to ensure correctness, efficiency, and reliability especially in regards to cultural idiosyncrasies.

Keywords

Artificial Intelligence ; Neural Machine Translation ; Source Input ; Statistical Machine Translation ; Target Output

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