Hybrid Approach to Text Translation in NLP Using Deep Learning and Ensemble Method

The major aim of AI is to enable robots to understand and interpret human discourse. Deep learning algorithms have considerably enhanced natural language processing, enabling it to do cutting-edge tasks like sentiment analysis, machine translation, and question answering. This paper offers a summary of current deep learning-based NLP research. The essential ideas of DL and its applications in language processing are initially introduced in this paper. It then reviews recent research in NLP, focusing on five major areas, including the modelling of languages, translation of languages, sentiment analysis, chatbots for queries, and generating text. For each area, the main techniques and models used, advantages, limitations, recent advancements, and future research directions are discussed. This paper concludes by discussing the challenges and providing a solution where in an image, the text is extracted in various ways and made in an appropriate format by using a deep learning approach. To further improve the translation quality, utilize an ensemble method that combines the outputs from multiple translation models trained using different architectures and parameters and highlights the potential impact of these advances in real-world applications.