Rather to my surprise, I found myself at a discussion of this very question at the Literary Translation Centre, in last week's London Book Fair 2022.
This is not my first brush with computer-aided-translation (CAT) tools. Back in the day (2000-2010, so quite a few days back!) I used to teach a CAT tools module on the Translation and Technology (Scientific, Technical and Medical) MSc, at Imperial College London.
First, let’s define some terms: CAT tools do many different things. Translation Memory (TM) apps create a database of segments (sentences or phrases) from the work of previous human translators and offer them up when the human translator comes across identical or similar phrases in a subsequent translation. TM apps are regularly used by companies producing instructions manuals and their translators. Imagine, for example, someone translating an instruction manual for a washing machine where most of the text for different models is repeated, but the spec differs. Note the human agency.
There’s Machine Translation (MT), something we scarcely touched on back then because the results were laughable even between European languages. But things have changed. Roy Youdale, of Bristol University, UK, who was one of the speakers at this talk, writes in a recent article ‘Can Artificial Intelligence Help Literary Translators?’ that ‘A game-changer …. has been the incorporation of machine translation (MT) into CAT tools.’ He goes on: ‘MT basically uses a computer to search and compare the words in a source text with very large databases (billions of words) of texts already translated into the target language. In addition to the translation of individual words, the computer searches for corresponding sequences of words or ‘strings’, a process known as ‘string matching’.’ Anyone who has used DeepL or Google Translate to get the gist of an online article written in a language they can’t read, will know that the results are often quite clear and well-worded.
