Proceedings of the Annual Meeting of the Cognitive Science Society

This work describes an approach for inferring Deterministic Context-free (DCF) Grammars in a C!onnectionist pjiradigm using a Recurrent Neural Network Pushdown Automaton (NNPDA). The N N P D A consists of a recurrent neural network connected to an external stack memory through a common error function. W e show that the N N P D A is able to learn the dynamics of an underlying pushdown automaton from examples of grammatical and non-grammatical strings. Not only does the network learn the state transitions in the automaton, it also learns the actions required to control the stack. In order to use continuous optimization methods, we develop an analog stack which reverts to a discrete stack by quantization of all activations, after the network has learned the transition rules and stack actions. W e further show an enhancement of the network's learning capabilities by providing hints. In addition, an initial comparative study of simulations with first, second and third order recurrent networks has shown that the increased degree of freedom in a higher order networks improve generalization bu not necessarily learning speed.