Learning Compositional Rules via Neural Program Synthesis

Understand

Many aspects of human reasoning, including language, require learning rules from very little data.

• Humans can do this, often learning systematic rules from very few examples, and combining these rules to form compositional rule-based systems.
• Current neural architectures, on the other hand, often fail to generalize in a compositional manner, especially when evaluated in ways that vary systematically from training.
• In this work, we present a neuro-symbolic model which learns entire rule systems from a small set of examples.