My first impression of the linked article is one of skepticism that they are really getting out of it what they think they are... While a computer program could certainly apply word relationships from an instruction manual to its interactions with a game program, presumably it has some method of characterizing and tracking word relevance as it "learns."
That very characterization process may actually contain all the necessary "learning," and the actual text be irrelevant. The real test they need to do, is not to compare it with a program that isn't using a text as a guide, but one that is using a completely irrelevant text as a guide. I think the learning may be happening entirely in their learning mechanism such that any text would work as well-- "wrong" advice would be characterized as such by trial and error, so even bad information is useful in a system like that.
I recall back in the 1960s or early 1970s, reading a children's craft project book of some kind that had a simple AI project where you could manually train a system of matchboxes containing colored candies, what the right answer for a given input pattern was (it was either a simple letter pattern recognizer, or would learn tic-tac-toe board patterns, I forget). But what I do remember, is even the "voters" in the population who consistently voted wrong provided useful information, if you can characterize them as always doing that. I wish I could remember what book it was in, but it did make some light bulbs turn on at the time. I suspect that the word-characterization process here might work just as well given ANY piece of text fed into it, it's just a source of data to characterize, and once the data is characterized, pretty much any data would produce the proper trained result. The manual text is then just a substrate on which to hang the learning information on, such that pretty much any substrate will do.