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Recurrent neural networks are a widely used class of neural architectures.They have, however, two shortcomings. First, they are often treated asblack-box models and as such it is difficult to understand what exactly theylearn as well as how they arrive at a particular prediction. Second, they tendto work poorly on sequences requiring long-term memorization, despite havingthis capacity in principle. We aim to address both shortcomings with a class ofrecurrent networks that use a stochastic state transition mechanism betweencell applications. This mechanism, which we term state-regularization, makesRNNs transition between a finite set of learnable states. We evaluatestate-regularized RNNs on (1) regular languages for the purpose of automataextraction; (2) non-regular languages such as balanced parentheses andpalindromes where external memory is required; and (3) real-word sequencelearning tasks for sentiment analysis, visual object recognition and textcategorisation. We show that state-regularization (a) simplifies the extractionof finite state automata that display an RNN's state transition dynamic; (b)forces RNNs to operate more like automata with external memory and less likefinite state machines, which potentiality leads to a more structural memory;(c) leads to better interpretability and explainability of RNNs by leveragingthe probabilistic finite state transition mechanism over time steps.

State-Regularized Recurrent Neural Networks to Extract Automata and Explain Predictions