In a way, it's literally nonsense, in the, uh, sense that it can't makey any sense of what it's translating and is just choosing some output string at random (i.e. with probability drawn from some distribution it has derived from its training examples).

Of course that's not what I mean above. Yes, the string I present is an example of the nonsense that comes out of purely statistical machine translation. It's a simple example and a very simple sentence, but that sort of noun-pronoun disagreement can completely distort the meaning of more complex sentences, not to mention longer passages where there are references to multiple persons across sentence boundaries.

Here's another example (original text by myself):

Πήγαμε με τη Βασιλική να δούμε το Μήτσο στο νοσοκομείο. Είχε σπάσει το πόδι του
και του βάλανε λάμες. Η Βασιλική, που είναι λίγο καψούρα μαζί του, του πήρε κάτι
σοκολάτες και λουλούδια, σα χαζό κοριτσάκι από καμμιά ρομαντική σειρά. Εγώ πήγα
για συμπαράσταση λαέ. Το χειρότερο ξέρεις ποιό είναι; Όχι μόνο ήτανε εκεί η μάνα
του, ο πατέρας του κι η αδερφή του, αλλά ήτανε κι η Μαρία, που τά 'χανε πέρσι.
Καταστροφή!

Google translates:

We went with Vassiliki to see Mitsos in the hospital. He had broken his leg
and he put blades on him. Basilica, who is a little bit of mascara with him,
got something chocolates and flowers, like a stupid little girl from any
romantic series. I went for support. Worst you know who it is? Not only was
the mother there his father and his sister, but she was Maria, who lost her
last year. Destruction!

My intended meaning:

Me and Vassiliki went to see Mitsos at the hospital. He had broken his leg
and he had to have metal plates inserted. Vassiliki, who has a bit of a crush
on him, got him some chocolates and flowers, like a silly little girl from some
rom-com. I just went along for solidarity. And you know what the worse part is?
His mother and father and his sister were there and not just them, but also Maria
his ex from last year. Disaster!

Examples like this reinforce my intuition that, in translation, it's impossible to get correct results while starting from the wrong premises. If you think about maths, for example. I'm pretty sure that you can train a statistical model (a translation model, actually) to try and generate results of various mathematical expressions. It's easy to see that such a model would often get the wrong results, because of a lack of any understanding of mathematics. In fact, you could very accurately say that it would produce a lot of nonsense. And even if it got the right answer- what would that prove? It would still be obvious that it doesn't know how to solve maths problems, just guess at their more likely solution.

It's also easy to see that we don't use statistical models to solve maths problems, because we don't need them: we know the rules of arithmetic etc that lead to the correct solutions. With translation, we don't know what those rules are (if they can even be represented as rules) so we go for statistical models instead- but that is only because we can't do anything better. Statistical machine translations are fundamentally flawed. They might be "good enough" for some situations, but they are effectively designed to fail, by not even trying to solve the actual problem of translation.

And so what they produce is nonsense- and it is nonsense even when it looks like it sorta, kinda makes sense. It's nonsense because it's basically, just random.