  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. Instead of
directly predicting outputs from inputs, we train our model to induce the
explicit system of rules governing a set of previously seen examples, drawing
upon techniques from the neural program synthesis literature. Our
rule-synthesis approach outperforms neural meta-learning techniques in three
domains: an artificial instruction-learning domain used to evaluate human
learning, the SCAN challenge datasets, and learning rule-based translations of
number words into integers for a wide range of human languages.
