| This is addressed in the white paper describing the project's architecture: 10.2 Machine translation Another widely used approach —mostly for readers,
much less for contributors— is the use of automatic translation services like
Google Translate. A reader finds an article they are interested in and then asks
the service to translate itinto a language they understand. Google Translate
currently supports about a hundred languages — about a third of thelanguages
Wikipedia supports. Also the quality of these translations can vary widely — and
almost never achieves thequality a reader expects from an encyclopedia [33,
86].* Unfortunately, the quality of the translations often correlates with the
availability of content in the given language [1],which leads to a Matthew
effect: languages that already have larger amounts of content also feature
better results intranslation. This is an inherent problem with the way Machine
Translation is currently trained, using large corpora. Whereas further
breakthroughs in Machine Translation are expected [43], these are hard to plan
for. In short, relying on Machine Translation may delay the achievement of the
Wikipedia mission by a rather unpredictabletime frame. One advantage Abstract Wikipedia would lead to is that Machine Translation
system can use the natural language generation system available in Wikilambda to
generate high-quality and high-fidelity parallel corpora for even morelanguages,
which can be used to train Machine Translation systems which then can resolve
the brittleness a symbolic system will undoubtedly encounter. So Abstract
Wikipedia will increase the speed Machine Translation will become better and
cover more languages in. https://arxiv.org/abs/2004.04733 (Theres's more discussion of machine learning in the paper but I'm quoting the section on machine translation in particular). |
What is the quality of open source translation these days?