For decades Computer Assisted Translation based on sentence translation memories has been the standard tool for going global. Although
CAT had been originally designed with a mid-90s PC in mind and there have been proposals for changing the underlying data model, the basic architecture of
CAT has been left unchanged. The dramatic advances in Neural Machine
Translation (NMT) have now made the whole product category obsolete.
NMT Crossing the Rubicon
While selling translation memory I always said, machines will only be able to translate once they understand text; and if one day they would, MT will be a mere footnote of a totally different revolution. Now it turns out that neural
networks, stacked deeply enough, do understand us sufficiently to create a
well formed translation. Over the last two years NMT has progressed
dramatically. It has now achieved “human parity” for important language pairs
and domains. That changes everything.
Industry Getting it Wrong
Most players in the $50b translation industry, service
providers but also their customers, think that NMT is just another source for a
translation proposal. In order to preserve their established way of delivery they
pitch the concept of “augmented translation”. However, if the machine translation
is as good (or bad) as human translation, who would you have revise it,
another translator or a subject matter expert?
Yes, the expert who knows what the text is about. The workflow is thus changing to automatic
translation and expert revision. Translation becomes faster, cheaper, and
better!
Different Actors, Different Tools
A revision UI will have to look very different to a CAT tool.
The most dramatic change is that a revision UI has to be extremely simple. To support the current model of augmented translation, CAT tools have become very powerful.
However, their complexity can only be handled by a highly demanded group of maybe a couple ten
thousand of professional translators globally.
For the new workflow a product design is required, that can support dozens of millions of, mostly occasional, expert revisers. Also, the revisers need to be pointed to the sentences which need revision. This requires multilingual knowledge.
Disruption Powered by Coreon
Coreon can answer the two key questions for using NMT in a professional
translation workflow: a) which parts of the translated text are not fit-for-purpose
and b) why not? To do so the multilingual knowledge system classifies
linguistic assets, human resources, QA, and projects in a unified system which
is expandable, dynamic, and provides fallback paths. In the future linguists
will engineer localization workflows such as Semiox and create multilingual knowledge in Coreon. "Doing
words” is left to NMT.
