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Title: Deep learning in machine translation: revolutionising language processing

Authors: Wei Xiu; Limei Gong; Zhifei Wang; Jianing Liu

Addresses: Mathematics and Computer Department, Chaoyang Normal University, Liaoning, Chaoyang, 122000, China ' Foreign Languages Department, Chaoyang Normal University, Liaoning, Chaoyang, 122000, China ' Mathematics and Computer Department, Chaoyang Normal University, Liaoning, Chaoyang, 122000, China ' Scientific Research Office, Chaoyang Normal University, Liaoning, Chaoyang, 122000, China

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.

Keywords: deep learning; machine translation; MT; neural networks; transformer models; language processing.

DOI: 10.1504/IJICT.2025.146374

International Journal of Information and Communication Technology, 2025 Vol.26 No.16, pp.1 - 15

Received: 17 Mar 2025
Accepted: 16 Apr 2025
Published online: 27 May 2025 *