  We provide a study of how induced model sparsity can help achieve
compositional generalization and better sample efficiency in grounded language
learning problems. We consider simple language-conditioned navigation problems
in a grid world environment with disentangled observations. We show that
standard neural architectures do not always yield compositional generalization.
To address this, we design an agent that contains a goal identification module
that encourages sparse correlations between words in the instruction and
attributes of objects, composing them together to find the goal. The output of
the goal identification module is the input to a value iteration network
planner. Our agent maintains a high level of performance on goals containing
novel combinations of properties even when learning from a handful of
demonstrations. We examine the internal representations of our agent and find
the correct correspondences between words in its dictionary and attributes in
the environment.
