WHAT DO WE MEAN WHEN WE SAY ... ?
Abbreviations and acronyms in the translation industry generally originate in the English language and are not necessarily self-explanatory. Here we explain some of the most frequently used abbreviations.
ANALYSIS (CAT supported)
The project manager uses the analysis to compare the text to be translated with existing translations.
CAT tools (see Part 1 of What do we mean when we say … ?) store the translated segments in a database so that they can be reused for future translations. The analysis calculates the number of already existing translation units and the number of repetitions within the file. The quote for the new translation and invoicing is based on this analysis. Intelligent creation of source documents can achieve significant savings in terms of delivery time and cost.
We never charge for repetitions and 100% or higher matches.
Context plays a key role when working with CAT tools. The percentage of a match indicates the similarity of a segment in the TM to a new segment.
CAT tools are intrinsically “stupid”. They store translations as an identical concordance of a source language segment with the target language segment. They do not recognize the context of the sentence. This difficulty can chiefly be solved by reviewing whether the segment just before or just after the relevant segment is also identical (101% match) or if both are identical (102% match).
Consistent industry- or customer-specific technical terminology.
The importance of consistent terminology cannot be emphasized enough. Nearly half of all translation errors and half of all post-processing work is caused by wrong or inconsistent terminology. These are technical terms the use of which in a translation project is obligatory. A glossary forms the basis of every translation. It is either provided by the customer or is created by the translator and then coordinated with the customer. With collaborative workflows (see below), this can now be done relatively easily and quickly. The initial (and admittedly somewhat tedious) effort quickly pays off because it reduces error sources, makes queries unnecessary, and achieves an optimum quality of the translation.
Cloud-based collaboration within the translation environment.
Most recent technical developments have changed the way in which we collaborate for the long-term. Tasks that used to be done in sequence or that required time-consuming communication are now integrated and overlap.
Within our SmartCAT translation environment, everyone participating in the translation project – translators, editors, project managers, terminologists – can simultaneously access all resources like TMs, TBs, dictionaries, etc., and everyone can communicate within this environment. In real time. The customer’s employees can easily be integrated. This saves time, improves quality, and turns collaboration into a smooth and pleasant process.
BACK TRANSLATION (BT)
Translating back into the source language.
A back translation is an additional step in assessing the quality of a translation by having an independent translator, who is not familiar with the original text, translate the translation back to the source language. A comparison of both texts then shows possible comprehension or translation errors of the first translation, which will have to be corrected.
This is a complex (and thus costly) step in quality assurance and it is generally used only for sensitive content (e.g. pharmacology, aeronautics) and cross-cultural marketing contexts.
Internet-based use of virtual resources.
Cloud computing is basically a technological relocation of the location where data is processed and stored: from local PCs to remote data servers, through an internet connection. Modern technologies have advanced to the point where a time delay can no longer be discerned. Extensive resources, maximum processing power, and networking with other users are clear advantages that enable collaborative workflows.
Concerns regarding data safety, which are frequently expressed, can be refuted because our data is safe, just like data stored on local or cloud-based servers at public authorities, insurances, banks, credit card companies, e-shops, etc.
Here you can find Part 1 I can’t make heads or tails of it and Part 2 That’s Greek to me of our series.
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