  As neural networks have dominated the state-of-the-art results in a wide
range of NLP tasks, it attracts considerable attention to improve the
performance of neural models by integrating symbolic knowledge. Different from
existing works, this paper investigates the combination of these two powerful
paradigms from the knowledge-driven side. We propose Neural Rule Engine (NRE),
which can learn knowledge explicitly from logic rules and then generalize them
implicitly with neural networks. NRE is implemented with neural module networks
in which each module represents an action of a logic rule. The experiments show
that NRE could greatly improve the generalization abilities of logic rules with
a significant increase in recall. Meanwhile, the precision is still maintained
at a high level.
