Neural Machine Translation

Neural Machine Translation (NMT) is the current state-of-the-art approach to automated translation, using artificial neural networks — specifically deep learning models — to produce translations that are significantly more fluent and natural-sounding than earlier machine translation technologies.

NMT systems are trained on large parallel corpora — collections of text that exist in both source and target languages. During training, the neural network learns patterns of how words, phrases, and sentences correspond between languages, developing an internal model of language structure and translation relationships. When presented with new text, the system uses these learned patterns to generate translations.

The key advancement of NMT over earlier approaches (rule-based and statistical machine translation) is its ability to consider entire sentences rather than translating word by word or phrase by phrase. This produces output that is more fluent and better captures the overall meaning of the source text. NMT can also handle longer-range dependencies in text, producing more coherent translations of complex sentences.

Major NMT engines include Google Translate, DeepL, Microsoft Translator, Amazon Translate, and various open-source models. Customised NMT engines can be trained on domain-specific content to improve performance for particular industries or content types.

Despite significant improvements, NMT still has limitations including difficulties with rare words and terminology, inconsistent handling of context across sentences, challenges with cultural adaptation and creative content, and variable quality across language pairs (particularly for lower-resource languages).

LEXIGO leverages NMT technology as a productivity tool within our professional translation workflows, using it to accelerate human translator output while maintaining the quality controls that ensure accurate, culturally appropriate results.

NMT has dramatically changed what is possible with machine translation, making it a viable productivity tool for professional translation workflows. Understanding NMT's capabilities and limitations helps organisations make informed decisions about when machine translation (with appropriate human oversight) can deliver value and when full human translation is the better investment.

For organisations evaluating translation approaches, NMT represents a powerful tool for specific use cases — but it is not a replacement for human translation expertise in contexts where accuracy, cultural sensitivity, and brand quality matter.