Compositional Generalization by Learning Analytical Expressions

Understand

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.