CAT Automated Translating
Can you imagine doing your job the same way you did 20 years ago? Probably not. That’s no different when it comes to translating.
However, technology also has its limits while translating from one language to another – people who often use Google Translate will have noticed this.
The tools professional translators use differ greatly from translation services such as Google Translate. We use so-called Translation Memories. These systems memorize the sentences that are translated. In some cases, the same sentence will appear in a different translation. Then we have a 100% match.
In addition, there are also partial matches. If one has translated the sentence “In England it also rains quite a bit in the summer” before and the sentence “In Scotland it also rains quite a bit in the summer” appears in a translation, the software informs the translator that a certain percentage of the words has been translated before.
This way one can ensure that identical texts will be translated identically and also save time (and money). But this also has its limits. A 100% match cannot always be translated the same way. Imagine you are translating a book in which an uncle asks his nephew “Do you like fish?”. The translation in German is simply: “Magst du gerne Fisch?”. However, the same question could be asked by an employee while having dinner with his employer: “Do you like fish?” - that is a 100% match, an easy job for the system: “Magst du gerne Fisch?”, but as a translation incorrect. In German, while speaking to your employer it is customary to address them as “Sie”, so the correct translation would be “Mögen Sie gerne Fisch?”. Here’s another example: “Initial Training” can be translated to “Anfängliche Schulungsmaßnahme” when it’s used in the context of training programmes in a company. But in a fitness club’s programme that would sound a bit out of place…
To cut a long story short: Even if parts of a text have been translated before, a human brain is always necessary for a good translation. The human input will surely become less, but will still be quite necessary in the foreseeable future. How is the system supposed to know when a shareholder in English is an Aktionär, or a Gesellschaftler in German? And when is the Gesellschafter a shareholder, and when a partner? Thankfully, we exist – and we ask you to be sure.