word2vec is trying to predict the co-occurence of words; if there is nothing translatable about it, then it will be effectively random. When you fit a maximum likelihood to random stuff and force it to attribute pattern to the noise, it's not going to come up with any sensible mapping.
But just because it's "untranslatable" from, say, Chinese to English, doesn't mean that we can't see many Chinese-Chinese co occurrences of the word, so this Chinese word would still have a vector of significance relative to other Chinese words, right?
That is- if you ran word2vec on a Chinese corpus, this word would be represented adequately in the vectorspace.
Wouldn't it then make sense, if you could find a proper mapping from one language's space to the other (I don't know if this part is realistic, but maybe by finding correspondences between already-translated words), that this word would not land in a bizarre spot were it to be expressed as a combination of vectors from the other language?
p.s. thanks for the reference to that thesis. I've been wondering about uncertainty in predictions for a side project to predict athletic performance that I'm working on, so this is very timely.