Machine Translation

Machine translation (MT) is the use of automated software to translate text from one language to another without direct human involvement in the translation process. MT has evolved through several technological generations, from early rule-based systems through statistical machine translation to the current state-of-the-art: Neural Machine Translation (NMT).

Neural Machine Translation, which became dominant from around 2016, uses deep learning models trained on vast quantities of parallel text (content that exists in both source and target languages). These models learn patterns of language use and can produce translations that are significantly more fluent and natural-sounding than earlier approaches. Major NMT engines include Google Translate, DeepL, Microsoft Translator, and Amazon Translate.

Despite significant improvements, MT has persistent limitations. It struggles with cultural nuance and adaptation, creative and persuasive content, humour and wordplay, context-dependent meaning (where the correct translation depends on surrounding content), highly specialised or domain-specific terminology, languages with limited training data (low-resource languages), and content that requires understanding of audience, intent, and emotional register.

MT is most useful for high-volume, low-risk content where speed matters more than polish — such as internal communications, content triage, or first-draft translation that will be reviewed and refined by human translators (post-editing).

LEXIGO uses MT strategically within our translation workflows, applying it where it adds genuine value — primarily as a productivity tool for professional translators — while ensuring that all client-facing output is reviewed and refined by qualified human linguists.

Machine translation is a powerful tool when used appropriately and a significant risk when misapplied. Using raw MT output for customer-facing communications, legal documents, or culturally sensitive content can result in embarrassing errors, compliance issues, and brand damage. Understanding where MT adds value and where it creates risk is essential for making informed decisions about your translation approach.

The most effective organisations use MT as one tool within a broader translation strategy, applying it where speed and cost matter most while investing in human translation for content where quality, accuracy, and cultural sensitivity are non-negotiable.