Neural machine translation (NMT)
Description
NMT represents a significant evolution in automated translation. Instead of translating word by word, neural models process complete sentences or segments, understanding syntax, semantics and tone to deliver coherent, human-like translations.
Modern NMT systems are built using encoder–decoder architectures and transformer models, which can capture long-range dependencies within language. They learn continuously, improving with every dataset and feedback loop. Domain adaptation allows NMT engines to specialize in technical, legal or life-sciences language while maintaining stylistic accuracy. A typical NMT workflow includes preparing bilingual data, training the neural model, translating new text and refining output through Machine translation post-editing (MTPE / post-editing) to ensure precision. When combined with human expertise, NMT allows organizations to translate at speed and scale without compromising quality.
Example use cases
- Documentation: Translating product documentation and technical manuals.
- Content: Localizing digital content across multiple languages.
- Chatbots: Powering multilingual chatbots and support systems.
- Submissions: Supporting regulatory submissions in life-sciences and legal sectors.
- Localization: Enabling continuous localization for global releases.
Key benefits
RWS perspective
RWS applies neural machine translation through Language Weaver, its secure, enterprise-grade NMT platform.
Language Weaver combines large-scale neural models with domain adaptation and custom training options, enabling clients to tailor translation output to their industry and brand voice. RWS brings together human linguistic expertise and intelligent automation to ensure that translations are both accurate and contextually relevant. By integrating Language Weaver with Trados and other enterprise systems, RWS helps organizations achieve continuous localization and high-quality multilingual delivery at scale.