Abstract
The integration of Natural Language Processing (NLP) with neural networks has transformed multilingual communication, enabling machines to understand and generate human language across various languages. This article explores how neural networks are leveraged to enhance NLP for multilingual applications, focusing on translation, sentiment analysis, and context understanding. It examines recent advancements in deep learning models, particularly Transformer-based models, that have significantly improved machine translation, language modeling, and cross-lingual communication. Furthermore, the article discusses the challenges in NLP for multilingual communication, including issues related to data scarcity, language resources, and model generalization.
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