How does machine translation work?
There are different types of machine translation, with neural machine translation currently seeing the widest use. Learn the characteristic features and key differences between neural, rule-based, and statistical machine translation.
Choosing the right provider also depends on whether you want to use a generic machine translation system (trained with enormous volumes of data from a wide variety of sources) or customizable machine translation systems (trained with your individual data).
Time and Money as a Factor for Success
Introducing machine translation does not mean that you will be able to save money starting from day one. It pays off in the long run, but it is first necessary to make some investments: You should plan to dedicate resources to the project, including personnel, and to build expertise in your organization. The chosen provider of the system wants to be paid, and the training data needs to be purchased.
Return on Investment
The implementation and operating costs of a customized system are higher than those of a generic system. However, this initial investment yields good returns over the years, as the quality of the raw translations will gradually get better and the post-editing process will become more efficient.
In this white paper, you can learn what types of machine translation exist, how machine translation engines are trained, and why it adds the most value if machine translation is used in combination with a translation management system.
- What machine translation systems are available
- How to train an MT engine to deliver high-quality results
- How you can successfully integrate post-editing into the translation process
- What you should pay attention to during the rollout