The Shortcomings of Natural Language Processing (NLP) Models and Their Applications in Achieving Accuracy in Translating from Arabic to English

Abstract

Translation is defined as the process of transferring meaning from one language to another. It is an extremely difficult and complex process because it involves not only transferring words but also ideas, culture, linguistic customs, and meanings derived from syntactic elements, their arrangement, word structure, and derivation. This is especially true in languages with complex structures, such as Arabic, which is characterized by its multiple linguistic contexts. These contexts have not been adequately addressed by NLP (Natural Language Processing) applications in machine translation models due to the lack of diverse contexts where metaphor, figurative language, grammatical inflections, morphological patterns and their connotations, and sentence structure all play pivotal roles in determining meaning. Furthermore, spoken language, with its inherent phonetic and expressive characteristics, conveys the text into broader semantic spaces. These spaces are influenced by the effect of intonation on specific syllables, the speaker's psychological state, the listener's mood, and accompanying body language, which transforms meaning into other subtle details. All of this, and more, is absent from machine translation, no matter how hard its creators try to imbue it with human emotions and feelings. This study highlights the importance of integrating in-depth linguistic analysis, contextual semantic modelling, and cultural awareness into natural language processing-based translation systems. By combining traditional linguistic insights with computational methods, the research offers a framework that can contribute to improving the accuracy of machine translation from Arabic to English.