Author(s)

Huang Wei

Corresponding Author

Huang Wei

Abstract

In the context of accelerating globalization, computer - aided translation technology is crucial for breaking down language barriers, and its translation accuracy is essential for the quality of information transfer. This paper conducts an in - depth study of machine translation, analyzes its principles, factors affecting accuracy, and proposes improvement strategies. Rule - based machine translation translates based on language rules, but it has difficulties in handling complex language phenomena and has high maintenance costs. Statistical machine translation leverages large - scale corpora and statistical models, yet it is significantly influenced by data quality and quantity. Neural machine translation applies deep - learning technology and has obvious advantages in semantic capture, but it also faces issues such as high data requirements and poor interpretability. The factors affecting translation accuracy include language complexity and similarity, corpus quality and scale, model architecture and training methods, and the choice of evaluation metrics. To improve translation accuracy, strategies such as optimizing the linguistic rule system, enhancing data quality and quantity, innovating model architectures and training methods, strengthening human - machine interaction and post - editing, and promoting interdisciplinary research and collaboration can be adopted. The research shows that although machine translation has made progress, continuous exploration in multiple aspects such as model architecture, data processing, human - machine collaboration, and interdisciplinary cooperation is still needed to promote the development of the technology and better serve global communication.

Keywords

Machine Translation; Translation Principles; Accuracy Factors; Improvement Strategies; Interdisciplinary Cooperation