11 months ago
April 18, 2018
Andrä Solutions Integrates DeepL, Systran NMT and Other Providers in Their TMS
More than the sum of its parts: TMS and MT combined
Berlin, 04-16-2018 – ONTRAM TMS now integrates all leading suppliers of machine translation (MT): Google Neural MT (GNMT), Microsoft Translator, Systran NMT, and, since February 2018, DeepL, as well. In August 2017, the Cologne-based company DeepL, formerly Linguee, introduced its MT solution DeepL Translator, which, through its outstanding results, caused a stir in the machine translation market. Through a dedicated interface, DeepL is now also available to all ONTRAM users.
The Translation Management System ONTRAM combines the latest language technologies to facilitate the management and preparation of translations. The quality of the suggestions increases over time, which is one of the key goals of MT integration. The optimal combination of TMS and MT engine speeds up the translation process, helping companies to handle more languages and make their product information more quickly available to new markets. This gives the ONTRAM customers important competitive advantages.
Machine Translation and ONTRAM significantly reduce translation efforts
Nowadays, using machine translation is common in corporations. As a result, integration into the translation management system plays a major strategic role. Machine translation is getting better and better and can now be easily integrated into the productive translation process. At the same time, this process is changing: simple translations no longer have to be done completely by a human translator. Rather, the translator can edit the suggestions after the translation by the machine, which means the human translator is taking on more of a role as an editor and copywriter. However, MT does not completely replace humans. The complexity and subtleties of language require people who continue to review and approve the results.
A TMS with an integrated MT engine can help accelerate the translation process and significantly reduce translation costs. At the same time, the MT engine is constantly learning: After completing a project, the MT engine remembers the approved translations and thus improves continuously.