We conduct experiments on synthetic, hand-authored, and human-paraphrased rule-bases to show promising results for QA and proof generation, with strong generalization performance.
model Interpretability: PROVER follows a significant body of previous work on developing interpretable neural models for NLP tasks to foster explainability.