  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 that
can identify patterns in streams of data. In this work we investigate their
effectiveness in inducing a regular grammar from data, without any assumptions
about the grammar. We train a recurrent neural network to distinguish between
strings that are in or outside a regular language, and utilize an algorithm for
extracting the learned finite-state automaton. We apply this method to several
regular languages and find unexpected results regarding the connections between
the network's states that may be regarded as evidence for generalization.
