Enhancing Cross-Language Communication with Recurrent Neural Networks in a Smart Translation System

The Smart Language Translation System (SLTS) is an innovative solution designed to bridge language barriers and facilitate effective communication across different languages. Language translation plays a crucial role in today's globalized world, where multilingual interactions are increasingly prevalent in various domains such as business, academia, and international relations. Traditional approaches to language translation relied on rule-based systems that involved extensive manual coding and linguistic analysis. However, these methods often struggled to handle the complexities of languages, leading to limitations in accuracy and scalability. This study tries to automate a language translation system using Deep Learning (DL). Further, this study utilizes two specific RNN architectures: Bidirectional Recurrent Neural Network (Bi-RNN) and Long Short-Term Memory (LSTM). These models are trained and evaluated using various metrics and performance measures, including BLEU (Bilingual Evaluation Understudy), precision, and NIST (Normalized Information Similarity Metric). The evaluation aims to identify the most effective RNN architecture. The study encompasses the entire translation pipeline, starting from preprocessing tasks such as cleaning, tokenization, encoding, and padding, followed by model construction training and evaluation. The results reveal the superiority of the Bi-RNN model over LSTM in terms of BLEU, precision, and NIST metrics across different word count ranges.