To be fair, Wikipedia basically appears in its entirety in the training data. It’s a good test to see whether the translation model and all the plumbing works well, but not whether the model generalises well.
I also think that the domain and the type of language used on Wikipedia is pretty consistent which will help a lot with unseen sentences.
By no means are these models bad! It’s just that Wikipedia is a particularly easy test for them.
Both of these methods have a bootstrapping problem, but at this point in the MT for many languages we have enough data to get started. Previous iterations of ParaCrawl used things like document structure and overlap of named entities among sentences to identify matching pairs. But this is much less robust. I don't know how they solve this problem today for low-resource languages.