Discrete Recurrent Neural Networks as Pushdown Automata
In this paper, we describe a new discrete recurrent model with discrete external stacks for learning context-free grammars (or pushdown automata).
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Discrete Recurrent Neural Networks for Grammatical Inference
Recurrent neural networks have recently been shown to have the ability to learn regular and context-free grammars from examples. We show that while conventional analog recurrent networks try to form clusters in activation space to represent discrete states of the grammars during learning, and can be successful in doing so, the clusters so formed tend to become unstable as longer and longer test input strings are presented to the network.
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