I've been thinking about a new approach to computational language learning for a while, and finally found time to write it down -- see the 2 page document here.
Pursued on its own, this is a "narrow AI" approach, but it's also designed to be pursued in an AGI context, and integrated into an AGI system like OpenCog.
In very broad terms, these ideas are consistent with the integrative NLP approach I described in this 2008 conference paper. But the application of evolutionary learning is a new idea, which should allow a more learning-oriented integrative approach than the conference paper alluded to.
Refining and implementing these ideas would be a lot of work, probably the equivalent of a PhD thesis for a very good student.
Those with a pure "experiential learning" bent will not like the suggested approach much, because it involves making use of existing linguistic resources alongside experiential knowledge. However, there's no doubt that existing statistical and rule-based computational linguistics have made a lot of progress, in spite of not having achieved human-level linguistic performance. I think the outlined approach would be able to leverage this progress in a way that works for AGI and integrates well with experiential learning.
I also think it would be possible for an AGI system (e.g. OpenCog, or many other approaches) to learn language purely from perceptual experience. However, the possibility of such an approach, doesn't imply its optimality in practice, given the hardware, software and knowledge resources available to us right now.