  We introduce a general method to extract knowledge from a recurrent neural
network (Long Short Term Memory) that has learnt to detect if a given input
sequence is valid or not, according to an unknown generative automaton. Based
on the clustering of the hidden states, we explain how to build and validate an
automaton that corresponds to the underlying (unknown) automaton, and allows to
predict if a given sequence is valid or not. The method is illustrated on
artificial grammars (Reber's grammar variations) as well as on a real use-case
whose underlying grammar is unknown.
