For some people, using a translation software program to translate a piece of text from one language to the next is enough. It would be naive to believe this always works. We have proven at Pangeanic that this works in applied contexts, when we are dealing with a particular domain, enough clean data and when certain conditions apply. Please refer to many of our presentations since 2009 on the use of applied machine translation to speed translation of documentation in particular.
But as we all know, it takes a lot more than just software. The application of unrestricted, universal machine translation will take some time. In fact, it would not be fair to talk about “machine translation” in general but language combinations (English/Spanish/French/Portuguese/Scandinavian) in which it is undoubtedly successful -whilst in other languages certain nuances make it less of a success story.
Also, in some types of texts, it is essential to depart greatly from the writer’s meaning in the original language in order to carry over the thoughts and sometimes even emotions behind the words. Metaphors and comparisions do not machine translate well many times.
When it comes to human translation, some joke it is one of the eldest professions in the world…
However, technology has focused in the last few decades in the need to help, accelerate and solve the needs of international commerce and information transfer (remember President Obama’s call upon coming into office). Particularly since the advent of Statistical Machine Translation and online services, translation has become part of our every day lives, truly ubiquitous.
The speed at which machine translation happens is a huge advantage in time over human translation. For information purposes only, most users will put up with errors from translation systems so at least they can make sense of some texts. But the real, true advantage of machine translation is as a productivity enhancer of human translation services.
Financially speaking, machine translation is usually more economical than paying for human translation, but both serve different purposes. Machine Translation has proven very useful for gisting (finding out) what a text is about. In some applications, it provides very good results, which means savings in time and money as humans post-edit the output. Even though translator resistance to become a post-editor has been historically a stumbling block, younger generations of linguists are becoming more and more used to edit fast machine output in what the experts call a “redefinition of the role of translation as a profession”.
The power of the human brain makes it possible to translate text whilst keeping the essence or context of what is being recorded. This results in a conversion that is much easier to read and understand, but at a much slower pace. If the text does not contain feelings or emotions, then translation systems can often produce good enough output to be acceptable as the technology has improved in comparison with old rule-based systems.
If you are trying to decide whether human or machine translation is the right one for you, then you should consider what type of content you have, an publication time you can afford so you can make the right choice.
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