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Caveats of machine translation

Translation tools (often referred to as CAT tools for ‘computer aided translation’) are a great means of streamlining some of the elements of a translation process, such as checking terminology or retrieving existing translations (so-called Translation Memories). Modern versions of these tools allow for a web-based, collaborative translation, giving collaborators such possibilities as revising and/or commenting proposed translations, evaluating existing translations or adding machine translation (henceforth MT) support. Modern MT systems are based on artificial neural networks, which have boosted quality considerably since roughly the mid-2010s.

CAT tools, with or without added machine translation support, have been studied from various angles. While they in general increase efficiency and often ease the task of translation as translators do not have to start from scratch, there are some caveats to be kept in mind when working with them. Here are some of the more important ones.

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Theoretical background to translation

This is the first of two blog posts with general notes on how to approach the task of translating science, touching upon the most prevalent basic notions and advice relevant to the task. The two main points presented in this and the upcoming post are (a) an introduction into a present-day functionalist view of translation which provides for a wide range of purpose-driven strategic translation options and (b) key caveats when making use of digital support tools for translation including machine translation. These general notes are meant for people who read academic texts at a postgraduate level and have experience with scholarly publishing, but may have little to no experience in translation. As technological tools are nowadays omnipresent in translation processes, they have been comprised here under basic background to translation.

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