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Grammar induction is the task of learning a grammar from a set of examples.Recently, neural networks have been shown to be powerful learning machines thatcan identify patterns in streams of data. In this work we investigate theireffectiveness in inducing a regular grammar from data, without any assumptionsabout the grammar. We train a recurrent neural network to distinguish betweenstrings that are in or outside a regular language, and utilize an algorithm forextracting the learned finite-state automaton. We apply this method to severalregular languages and find unexpected results regarding the connections betweenthe network's states that may be regarded as evidence for generalization.

Inducing Regular Grammars Using Recurrent Neural Networks