http://norvig.com/chomsky.html
The essential question is: Can it go from basic reading comprehension to advanced just by adding more rules. Is intelligence simply 10 million rules? If so, how do we go about creating new rules as language evolves? By hard-coding them, as in the example code?
The real test for general AI and NLP is how well it, well, generalizes; i.e. how well does it deal with situations we have not explicitly anticipated?
In my opinion, the fuzzy, statistical methods @davesullivan mentions have a better chance at generalizing (although they may well be augmented by rules-based AI).
If an AI doesn't have a good way of transferring what it knows to novel problems, then it is severely limited. It's treating the world like a canned problem with a finite number of possibilities, like chess or checkers, when in fact the world is much more complex.
The way DeepMind combines deep learning and reinforcement learning is one way of acknowledging that complexity.
Deep learning learns patterns in raw sensory data, which means it can ingest and handle the new. Reinforcement learning learns to perform actions over a series of unknown states, improving its choices by monitoring the rewards it receives for those actions. They both maximize within uncertainty, and I think that's our best bet going forward.
Because the world, and language, cannot be known in their entirety. The number, motion and interrelation of the atoms of air in the room where I'm typing this are all too large and complex to be computable. Their fluid dynamics can only be vaguely guessed at, not deterministically predicted in a few lines of code.
The trick will be to bridge the gap between the hard-coded, limited rules and the unlimited recombinations of language, which is inventing new rules and words all the time.