I can't say I can point to any sort of evidence of this, so take it with a pinch of salt, but my gut feeling is that the people in Google who are responsible for GT are somewhat fanatical about making purely statistical machine translation (and, in particular, deep-learning machine translation, a.k.a. neural machine translation) work. They are most probably perfectly well aware that it is possible to improve their system by using some rule-based fall-backs, or some background knowledge, but they have decided that they dont' want to use those and instead want to do machine translation from scratch and from data only, without any expert input.
So it sucks, like all pure systems suck.
This is like complaining that Word isn’t as good as vi, which is decades old, for text editing. Sure they could bolt a text editor onto Word if they wanted to, but why bother? That’s not what people are looking for when they use the product. And if that’s what you want, there are many other choices that are much better.
I would start by determining the number of words in the query. If it's equal to 1, the user probably wants to translate a single word, maybe give him the 1:1 dictionary result. If there's more, the other words possibly provide context, so let's use the sophisticated thing.
Edit: I did not want to complain about the translation quality nor show my sense of entitlement. You have authoritatively stated and used a some jargon to tell us it doesn't use English when translating between languages, so I simply wanted to understand more about this process because, on the surface, it looks like English was involved in my specific example.