Machine translation

Machine translation has transformed from a curiosity into a practical tool that most software localization teams now use daily. Language Monster integrates with both Amazon Web Services (AWS) and Microsoft Azure, giving you access to two of the most capable neural machine translation engines available. Rather than locking you into a single provider, this choice lets teams select the engine that performs best for their specific language pairs and content types.

The most effective use of machine translation in software localization is not wholesale replacement of human translators — it is intelligent pre-population. When new strings are pushed to a project, machine translation can generate a first-draft translation immediately. Translators then review, correct, and approve rather than starting from a blank page, which significantly reduces the time and cost of reaching a completed, high-quality translation.

Language Monster applies machine translation at the key level, meaning each string is handled individually rather than translated as a block of text. This is important for software content, where strings are often short, context-dependent, and structurally varied. Key-level translation also ensures that placeholders, variables, and formatting tokens in your strings are preserved correctly rather than being disrupted by the translation engine.

Machine translation works best when it is guided by the rest of your project data. When Language Monster applies machine translation, it takes your existing Glossary into account, so brand terms and approved vocabulary are respected even in automated drafts. Translation Memory is checked first, so any string with an existing high-confidence match is reused rather than re-translated by the engine.

The practical result is a translation workflow that moves faster without sacrificing quality gates. Machine translation handles the high-volume, low-complexity work; human review catches the cases where the engine falls short. Language Monster gives you the controls to decide exactly where each approach applies in your project.