Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce unpredictability of the neural models. We propose a general framework capable of enhancing various types of neural networks (e.g., CNNs and RNNs) with declarative first-order logic rules. Specifically, we develop an iterative distillation method that transfers the structured information of logic rules into the weights of neural networks. We deploy the framework on a CNN for sentiment analysis, and an RNN for named entity recognition. With a few highly intuitive rules, we obtain substantial improvements and achieve state-of-the-art or comparable results to previous best-performing systems.
Hu et al. proposed a framework using logic rules to obtain more meaningful network representations by constructing an iterative distillation method that transfers the structured information of logic rules into the weights of neural networks REF .
Harnessing Deep Neural Networks with Logic Rules
ArXiv
ArXiv
Computer Science Mathematics