Harnessing Deep Neural Networks with Logic Rules
Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard H. Hovy, Eric P. Xing · 2016
Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability 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.