  We introduce a neuro-symbolic natural logic framework based on reinforcement
learning with introspective revision. The model samples and rewards specific
reasoning paths through policy gradient, in which the introspective revision
algorithm modifies intermediate symbolic reasoning steps to discover
reward-earning operations as well as leverages external knowledge to alleviate
spurious reasoning and training inefficiency. The framework is supported by
properly designed local relation models to avoid input entangling, which helps
ensure the interpretability of the proof paths. The proposed model has built-in
interpretability and shows superior capability in monotonicity inference,
systematic generalization, and interpretability, compared to previous models on
the existing datasets.
