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The capability of making interpretable and self-explanatory decisions isessential for developing responsible machine learning systems. In this work, westudy the learning to explain problem in the scope of inductive logicprogramming (ILP). We propose Neural Logic Inductive Learning (NLIL), anefficient differentiable ILP framework that learns first-order logic rules thatcan explain the patterns in the data. In experiments, compared with thestate-of-the-art methods, we find NLIL can search for rules that are x10 timeslonger while remaining x3 times faster. We also show that NLIL can scale tolarge image datasets, i.e. Visual Genome, with 1M entities.

Learn to Explain Efficiently via Neural Logic Inductive Learning