What are the prospects for achieving human-like language learning in machines?  There is a growing recognition that statistical learning will have to be linked to perceptual and conceptual modeling of the world.  Recent work in the area of grounded language learning is moving in that direction. For example, Kim and Mooney (2012) describe methods of using sentences paired with graph-based descriptions of actions and contexts to hypothesize PCFG rules for parsing NL instructions into action representations, while learning rule probabilities with the inside-outside algorithm. However, they assumed a very restricted domain, and the question remains how far the modeling of perception, concept formation, and of semantic and episodic memory needs to be taken to support unrestricted language learning.  As in the case of world knowledge acquisition by machines (see the preceding section), the modeling capabilities may need to achieve equivalence with those of a newborn, allowing for encoding percepts and ideas in symbolic and imagistic languages of thought, for taxonomizing entity types,  recognizing animacy and intentionality, organizing and abstracting spatial relations and causal chains of events, and more. Providing such capabilities is likely to require, along with advances in our understanding of cognitive architecture, resolution of the very issues concerning the representation and use of linguistic, semantic, and world knowledge that have been the traditional focus in computational linguistics. 