  How can neural networks perform so well on compositional tasks even though
they lack explicit compositional representations? We use a novel analysis
technique called ROLE to show that recurrent neural networks perform well on
such tasks by converging to solutions which implicitly represent symbolic
structure. This method uncovers a symbolic structure which, when properly
embedded in vector space, closely approximates the encodings of a standard
seq2seq network trained to perform the compositional SCAN task. We verify the
causal importance of the discovered symbolic structure by showing that, when we
systematically manipulate hidden embeddings based on this symbolic structure,
the model's output is changed in the way predicted by our analysis.
