Neural Machine Translation
Neural machine translation is based on an artificial neural network (ANN) that mimics the neural connections in the brain. This approach, too, involves the analysis of parallel corpuses for the translation. The difference is that in the ANN, the grammatical context of the sentences is implicitly taken into consideration.
As translations created with NMT are very pleasant to read, there is a risk of overlooking errors. Nevertheless, neural machine translation is the approach that currently delivers the best results. Therefore, a more careful review at the lexical level is usually worth the effort.
Generic and Customizable MT Systems
The scope of the project for the introduction of a machine translation system greatly depends on one factor, namely on the decision whether generic or customizable engines are to be used.
The main characteristic of generic systems is that they are trained with huge amounts of data from various domains. As a result, the translations are very fluent, but the terminology may not suit every subject area, or a mistranslation may even occur in a particular domain due to the lack of training data.
By contrast, customizable systems are trained with customer-specific data in order to take both the terminology and the corporate language into consideration in the translations. These engines deliver better raw translations that require less post-editing.
Success Factor Time and Money
Introducing machine translation does not mean that you will be able to save money from Day One on. Though machine translation is worthwhile in the long run, money needs to be invested initially: An additional project manager may need to be hired or trained, the provider of the MT system needs to be paid for his services, the training data need to be purchased, and the translators and post-editors need to be trained and paid.
Return on Investment
The implementation and operating costs of a customized system are considerably higher than those of a generic system. However, this initial investment will pay in the long run, as the quality of the raw translations will gradually get better and the post-editors will work faster.
- what differences there are between various machine translation systems
- which factors play a role in selecting and introducing a translation system
- what the opportunities and risks of machine translation (MT) are
- how enterprises can successfully establish post-editing in the translation process
- how you can protect your confidential data when using machine translation
- how an MT engine is trained in order to deliver high-quality translations
- why an MT engine should be connected to a translation management system