Neural Machine Translation (NMT)
A machine translation approach using deep neural networks to learn translation patterns from large bilingual datasets, producing more natural and contextually accurate translations than traditional methods.
Understanding Neural Machine Translation (NMT)
Neural machine translation represents the current state of the art in automated language translation. Unlike earlier statistical or rule-based approaches, NMT uses deep learning models (typically transformer architectures) that learn to translate by studying millions of parallel sentences in two languages. NMT models capture context, grammar, and even some cultural nuances, producing translations that read more naturally and accurately than previous generations of machine translation. The technology was popularized by Google's 2016 introduction of their NMT system and has since been adopted across the translation industry.
How Selah Translate Uses Neural Machine Translation (NMT)
Selah Translate's translation engine is built on neural machine translation technology. The AI models have been trained on diverse multilingual datasets including religious texts, business documents, academic papers, and conversational data to provide accurate translations across the 30+ supported languages.