  Human-designed rules are widely used to build industry applications. However,
it is infeasible to maintain thousands of such hand-crafted rules. So it is
very important to integrate the rule knowledge into neural networks to build a
hybrid model that achieves better performance. Specifically, the human-designed
rules are formulated as Regular Expressions (REs), from which the equivalent
Minimal Deterministic Finite Automatons (MDFAs) are constructed. We propose to
use the MDFA as an intermediate model to capture the matched RE patterns as
rule-based features for each input sentence and introduce these additional
features into neural networks. We evaluate the proposed method on the ATIS
intent classification task. The experiment results show that the proposed
method achieves the best performance compared to neural networks and four other
methods that combine REs and neural networks when the training dataset is
relatively small.
