Design of English Machine Translation Models Based on Deep Neural Network Algorithms

To enhance the accuracy and fluency of English machine translation, this study designed a translation model based on deep neural network algorithms. Utilizing an encoder-decoder architecture with an integrated attention mechanism, the model's ability to capture linguistic context is significant...

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Bibliographic Details
Published in:IEEE Information Technology, Networking, Electronic and Automation Control Conference (Online) Vol. 7; pp. 1441 - 1445
Main Author: Ye, Ying
Format: Conference Proceeding
Language:English
Published: IEEE 20.09.2024
Subjects:
Analytical models
Artificial neural networks
Attention mechanisms
Convolutional Neural Networks (CNN)
Data models
Deep learning
Deep neural network algorithms
English machine translation models
Machine translation
Solid modeling
Solids
Training
ISSN:2693-3128
Online Access:Get full text
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Summary:To enhance the accuracy and fluency of English machine translation, this study designed a translation model based on deep neural network algorithms. Utilizing an encoder-decoder architecture with an integrated attention mechanism, the model's ability to capture linguistic context is significantly improved. Various aspects such as data preprocessing, model architecture design, training processes, and model optimization were analyzed through comparative studies. The results demonstrate that our model outperforms traditional models on standard datasets, conclusively proving the effectiveness and value of deep learning technology in the field of machine translation.
ISSN:2693-3128
DOI:10.1109/ITNEC60942.2024.10733009