ADVANCING MACHINE TRANSLATION: CHALLENGES, INNOVATIONS, AND THE FUTURE OF NLP IN LOW-RESOURCE LANGUAGES
Keywords:
Natural Language Processing, Machine Translation, Deep Learning, Neural Networks, Statistical Methods, Low-Resource Languages, Transformer Models, Computational Linguistics, Cross-Lingual Understanding, Ethical AI, Contextual Translation, Language Modeling, Multimodal Translation.Abstract
This article explores the advancements and challenges in Natural Language Processing (NLP), particularly in the domain of machine translation. It discusses various methodologies, analyzes their effectiveness, and highlights future directions in NLP-driven translation technologies. The study is based on a comparative analysis of rule-based, statistical, and neural machine translation models. The results demonstrate the significant improvements made by deep learning approaches while addressing existing challenges such as low-resource language translation and contextual accuracy. Additionally, the paper explores real-world applications of machine translation, including business communication, legal documentation, and educational accessibility, to highlight the impact of translation technologies. Furthermore, it discusses ethical concerns, including bias in machine translation, data privacy, and the implications of automated translations replacing human translators in professional sectors.
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