In-Depth Exploration of the Advantages of Neural Networks in English Machine Translation
Linlin Li · 2024
Machine translation, an emerging field in artificial intelligence, has attracted considerable attention from academia and industry. This study presents an in-depth investigation of the advantages of neural networks for English machine translation. The study first reviews state-of-the-art neural network models and proposes an innovative methodology for efficient neural network-based machine translation. The proposed approach includes text semantic understanding, a neural network-based translation algorithm, and the use of deep belief networks (DBFs) for feature extraction. The study conducts experiments using a data-set consisting of 3000 pairs of corpora from the different repositories, including historical documents and online resources. The study evaluates the translation performance of our proposed model against popular translation tools such as Google and Baidu. The experimental results demonstrate the superiority of our proposed model in terms of translation quality.