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Learning Reasoning Strategies in End-to-End Differentiable Proving
Pasquale Minervini · Sebastian Riedel · Pontus Stenetorp · Edward Grefenstette · Tim Rocktäschel

Tue Jul 14 02:00 PM -- 02:45 PM & Wed Jul 15 01:00 AM -- 01:45 AM (PDT) @

Attempts to render deep learning models interpretable, data-efficient, and robust have seen some success through hybridisation with rule-based systems, for example, in Neural Theorem Provers (NTPs). These neuro-symbolic models can induce interpretable rules and learn representations from data via back-propagation, while providing logical explanations for their predictions. However, they are restricted by their computational complexity, as they need to consider all possible proof paths for explaining a goal, thus rendering them unfit for large-scale applications. We present Conditional Theorem Provers (CTPs), an extension to NTPs that learns an optimal rule selection strategy via gradient-based optimisation. We show that CTPs are scalable and yield state-of-the-art results on the CLUTRR dataset, which tests systematic generalisation of neural models by learning to reason over smaller graphs and evaluating on larger ones. Finally, CTPs show better link prediction results on standard benchmarks in comparison with other neural-symbolic models, while being explainable.

Author Information

Pasquale Minervini (University College London)
Sebastian Riedel (UCL)
Pontus Stenetorp (University College London)
Edward Grefenstette (Facebook AI Research / UCL)
Tim Rocktäschel (Facebook AI Research & University College London)

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