Year: 2025
Type: article
Abstract: Machine translation (MT) has undergone a remarkable transformation with the rise of deep learning methods, significantly improving translation accuracy and fluency. This paper examines how deep learning methodologies have influenced MT, particularly through the use of neural networks, Seq2Seq models, attention mechanisms, and transformer architectures. Traditional rule-based or statistical approaches have evolved into neural machine translation (NMT), leveraging large-scale data and advanced learning paradigms. The study highlights advancements, challenges, and future prospects, focusing on low-resource language translation, model bias, and computational efficiency. By analysing current developments and trends, this paper emphasises the revolutionary role of deep learning in enhancing multilingual communication through machine translation.
Authors: Wei Xiu, Limei Gong, Zhifei Wang, Jianing Liu
Institution: Chaoyang Normal University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410826683