Year: 2025
Type: article
Abstract: Advancements in Natural Language Processing (NLP) have significantly improved multilingual communication through machine translation, text-to-speech conversion, and cross-language information retrieval (CLIR) [1]-[5]. Various approaches, including rule-based and statistical models, enhance translation accuracy and language identification [6]-[8]. Neural machine translation (NMT) and deep learning techniques further refine speech recognition and sentiment analysis [9]- [12]. Structural differences in languages, such as Subject-Verb-Object (SVO) versus Subject-Object-Verb (SOV) order, influence translation efficiency [13]-[16]. Additionally, AI-driven systems contribute to real-time speech synthesis and automated text processing [17]-[19]. This paper consolidates research on multilingual NLP applications and proposes improvements in translation models for better contextual understanding. Future work will focus on optimizing neural translation frameworks for enhanced accuracy and adaptability [20]-[22].
Author: Prof. Mirza Moiz Baig
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409924786