A commonly employed technology for machine translation is neural networks, particularly using deep learning techniques. This approach enables improved accuracy and fluency by analyzing large datasets of bilingual text to learn the context and semantics of languages. Systems like Google Translate leverage neural machine translation, allowing for more nuanced translations through understanding sentence structures and idiomatic expressions. This advancement has significantly enhanced automated translations, making them more relevant and user-friendly compared to traditional rule-based methods.