The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: “Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?”. In answer, we develop novel methods to exploit the rich expressiveness of REs at different levels within a NN, showing that the combination sig-nificantly enhances the learning effectiveness when a small number of training examples are available. We evaluate our approach by applying it to spoken language understanding for intent detection and slot filling. Experimental re-sults show that our approach is highly effective in exploiting the available training data, giving a clear boost to the RE-unaware NN.