Authors: Sharath Chandra Kodtihyala, Riddhi kinnera, Shashikiran Sangisetti, Praveem Kumar, Prasanthrao A
Abstract: This study presents Polyglot, a deep learning-based language translation system that utilizes Transformer, LSTM, Attention, and Seq2Seq models to enhance context-aware translation. While well-known systems like Google Translate provide reliable translations, Polyglot offers improved contextual understanding through a hybrid approach that balances accuracy and efficiency. The study evaluates Polyglot’s performance using BLEU scores and user satisfaction, demonstrating its effectiveness. It includes a detailed discussion on the dataset, model architecture, training process, and evaluation criteria. The results indicate a significant improvement in translation quality compared to baseline models. Future work will focus on real-time improvements and customization to further enhance translation accuracy and user experience
