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We present a novel algorithm that uses exact learning and abstraction toextract a deterministic finite automaton describing the state dynamics of agiven trained RNN. We do this using Angluin's L* algorithm as a learner and thetrained RNN as an oracle. Our technique efficiently extracts accurate automatafrom trained RNNs, even when the state vectors are large and require finedifferentiation.

Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples