  Compositional generalization is a basic and essential intellective capability
of human beings, which allows us to recombine known parts readily. However,
existing neural network based models have been proven to be extremely deficient
in such a capability. Inspired by work in cognition which argues
compositionality can be captured by variable slots with symbolic functions, we
present a refreshing view that connects a memory-augmented neural model with
analytical expressions, to achieve compositional generalization. Our model
consists of two cooperative neural modules, Composer and Solver, fitting well
with the cognitive argument while being able to be trained in an end-to-end
manner via a hierarchical reinforcement learning algorithm. Experiments on the
well-known benchmark SCAN demonstrate that our model seizes a great ability of
compositional generalization, solving all challenges addressed by previous
works with 100% accuracies.
