  We present a method to extract a weighted finite automaton (WFA) from a
recurrent neural network (RNN). Our algorithm is based on the WFA learning
algorithm by Balle and Mohri, which is in turn an extension of Angluin's
classic \lstar algorithm. Our technical novelty is in the use of
\emph{regression} methods for the so-called equivalence queries, thus
exploiting the internal state space of an RNN to prioritize counterexample
candidates. This way we achieve a quantitative/weighted extension of the recent
work by Weiss, Goldberg and Yahav that extracts DFAs. We experimentally
evaluate the accuracy, expressivity and efficiency of the extracted WFAs.
