Structured representations developed by learning in recurrent networks

We study the internal representations developed by recurrent network learning to associate semantic role descriptions with simple sentences. Our analysis of the state-space of the hidden units makes explicit the structure of the representations and shows how it reflects the similarities between the sentences. We find that the representation scheme developed by learning is related to a formally defined one