If it turns out that the easiest, or even only means of doing this is by emulating the human brain, then it is entirely possible that we inherit a whole new set of constraints and dependencies such that world-simulation and an emobdied mind are required to make such a system learn. If this turns out not to be the case, that there's some underlying principle of language we can emulate (the classic "airplanes don't fly like birds" argument) then it may be the case that text is enough. But that's in the presence of a new assumption, that our system came pre-equipped to learn language, and didn't manufacture an understanding from whole cloth. That the model weights were pre-initialized to specific values.
I don't think it's so weird to imagine that natural language really doesn't convey a ton of explicit information on its own. Sure, there's some there, enough that our current AI attempts can solve little corners of the bigger problem. But is it so strange to imagine that the machinery of the human brain takes lossy, low-information language and expands, extrapolates, and interprets it so heavily so as to make it orders of magnitude more complex than the lossy, narrow channel through which it was conveyed? That the only reason we're capable of learning language and understanding eachother (the times we _do_ understand eachother) is because we all come pre-equipped with the same decryption hardware?
1) They appear to have crafted the skeleton of a grammar as it is with their nodes, super nodes, and slot collocations. This is directly analogous to something like an Xbar grammar, and is not learned by the system; therefore, if anything, it's strengthening a Chomskian position; the system is learning how a certain set of signals satisfy its extant constraints.
2) The don't appear to go beyond generative grammar, which already seems largely solvable by other ML methods, and is a subset of the problem "language". Correct me if I'm wrong here, it's a very long paper and I may have missed something.
There's also the related phenomenon of "subtext" -- the idea that some language has a different meaning than what's said. For instance, when I ask about whether a signature line on a form is required, and the other person says, "Yes, it's required. However you think best to get the signature." There's a subtext there of, "This signature won't actually be checked, so don't worry about it."
Of course we have no idea how the Voynich manuscript is encrypted (which would make the assumptions of word2vec wrong), or if it even has any meaning at all. And it's an incredibly small dataset compared to modern text corpuses, so there is probably significant uncertainty and overfitting. And other problems like inconsistent spellings, many errors in transcriptions, etc. But in principle this is a good strategy.
>how would a person do if they were locked in a room with lots of books written in a language unknown to them?
If you spent all day reading them, for years, and you somehow didn't get bored and kept at it, eventually you would start to see the patterns. You would learn how "slithy toves" are related to "brillig", even if you have no idea how that would translate to English. Study it long enough, and you may even be able to produce text in that language, indistinguishable from the real text. You may be able to predict the next word in a sentence, and identify mistakes, etc. Perhaps carry out a conversation in that language.
And I think eventually you would understand what the words mean, by comparing the patterns to those found in English. Once you have guesses for translations of just a few words, you can translate the rest. Because you know the relationships between words, and so knowing one word constrains the possibilities of what the other words can be.
If the translation it produces is nonsense, the words you guessed must have been wrong, and you can try again with other words. Eventually you will find a translation that isn't nonsense, and there you go. This would be very difficult for humans, because the number of hypotheses to test is so large, and analyzing text takes forever. Computers can do it at lightspeed though.
More generally, has any attempt been made to identify the meanings of words in any sufficiently large corpus of text in a known foreign language (for example, Finnish), without being provided with a translation into English, and then compare the identified meanings to the actual meanings, as a first step towards translation?
Doing this without any translated words at all, would be more difficult. But I believe possible. It's actually a project I want to try in the near future.
I don't understand how this could possibly be the case. We can already make great progress on language understanding with simple methods like word2vec, or perhaps even markov chains. There are tons of statistical patterns in text that can be learned by computers.