Research on the Application of Machine Learning in Translation Systems

With the rapid development of globalization, machine translation systems have become indispensable tools for cross-linguistic communication. This paper explores the integration of machine learning technologies in translation systems, utilizing datasets including high-resource language pairs, low-resource language pairs, and domain-specific corpora to establish training and evaluation frameworks for different technical approaches. It focuses on the evolutionary progression from Statistical Machine Translation (SMT) to Neural Machine Translation (NMT) and the latest pre-trained models. Experimental results demonstrate that deep learning models significantly outperform traditional statistical methods, with the Transformer model achieving state-of-the-art performance through parallel computing and global context modeling. The paper also discusses current challenges such as low-resource language processing, domain adaptation, and interpretability, providing insights for future research directions.