The greatest problem besetting translation software developers is quality. It is no secret that the main objective of machine translation is to sound as close to human translation as possible – a person must not be able to tell that the text was written by a machine (while in fact, it was).

One of the most frequently asked questions is: can machine translation fully replace human translation? Well, yes and no.

Obviously, some texts require human intervention, e.g. in choosing the best meaning of a certain word, so as to avoid ambiguities and bloopers. These factors may sound quite discouraging but “artificial” translation has its pros, mainly in the field of marketing – increased productivity, speed of process and less money for maintaining and performing tasks. Companies involved in the development of machine translations have put quality as their number one objective – however, at this stage the quality of human translation cannot be achieved.

Machine translation can offer a better quality than a standard translation only when the following conditions apply:

  • Expert introduction of the accurate terminology;
  • Grammatical and terminological accuracy of the source texts;
  • Automatic standardization of source texts;
  • Correct input of existing translation memory;
  • Trained post-editors;
  • Continuous dictionary updates, guaranteeing the correct use of a new/modified terminology;
  • Automatic post-production;
  • Continuous update of the “translation memory”, guaranteeing improved contents.


The software defines the quality of work to a considerable degree – there are a lot of programs specialized in machine translation. Some of the best-known are Apertium, OpenLogos, SYSTRAN, the notorious Google Translate, etc.

The quality of such programs is priority number one for developers. In the most commonly used working method, SMT (statistical machine translation), quality is improved by continuous adding of information to the translation memory, terminology and the generic dictionary, used by the software as an example, in order to generate an adequate translation. The finishing touch is coding and testing of the result while the system is finely set. Almost the same applies for the rule based method which is no longer that effective.

In recent years SMT has become the most developed machine translation method. There are a lot of other methods but currently this is the most effective one, it shows the most promising results and covers the greatest number of texts for translation. These features add reliability to SMT, which is necessary for the much desired effect – to make machine translation indistinguishable from human translation.