  We propose an efficient interpretable neuro-symbolic model to solve Inductive
Logic Programming (ILP) problems. In this model, which is built from a set of
meta-rules organised in a hierarchical structure, first-order rules are
invented by learning embeddings to match facts and body predicates of a
meta-rule. To instantiate it, we specifically design an expressive set of
generic meta-rules, and demonstrate they generate a consequent fragment of Horn
clauses. During training, we inject a controlled \pw{Gumbel} noise to avoid
local optima and employ interpretability-regularization term to further guide
the convergence to interpretable rules. We empirically validate our model on
various tasks (ILP, visual genome, reinforcement learning) against several
state-of-the-art methods.
