Abstract
How to utilize symbolic knowledge in deep learning is an important problem. Deep neural networks are flexible and powerful, while symbolic knowledge has the virtue of interpretability and intuitiveness. It is necessary to combine the two together to inject symbolic knowledge into neural networks. We propose a novel approach to combine neural networks with logic rules. In this approach, task-specific supervised learning and policy-based reinforcement learning are performed alternately to train a neural model until convergence. The basic idea is to use supervised learning to train a deep model and use reinforcement learning to propel the deep model to meet logic rules. In the process of the policy gradient reinforcement learning, if a predicted output of a deep model meets all logical rules, the deep model is given a positive reward, otherwise, it is given a negative reward. By maximizing the expected rewards, the deep model can be gradually adjusted to meet logical constraints. We conduct experiments on the tasks of named entity recognition. The experimental results demonstrate the effectiveness of our method.
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