Machine Translation (MT) has been around for decades, yet has undergone successive revolutions: the advent of the web (and web-based platforms), its integration with social media, and massive advances in Machine Learning (ML). Even so, MT still can’t make the same level of decisions as a human. Therefore, e2f believes in a hybrid model, where MT services are combined with the best of human editorial and quality oversight.

These hybrid methods are best suited to help with large volume content such as manuals, documentation, catalogs and user-generated content.

Autoadaptive Machine Translation

Autoadaptive MT is an emerging class of systems wherein human translators are empowered by tools that contextually provide suggestions: predictive typing and autocomplete can be used for translation across languages. Moreover, underlying Machine Learning (ML) engines learn dynamically and interactively based on translator decisions, rather than simple static analysis.

Case Study: Auto-Adaptive MT

Machine Translation Post-Editing (MTPE)

Machine Translation Post-Editing (MTPE) works by having human oversight over MT output. Original sources are translated en masse by machine translation (MT) engines. Then our trained and fluent reviewers edit the resultant content for readability and accuracy. e2f can assist you in selecting and/or implementing a machine translation engine.

Learn More About MTPE