  This work investigates if the current neural architectures are adequate for
learning symbolic rewriting. Two kinds of data sets are proposed for this
research -- one based on automated proofs and the other being a synthetic set
of polynomial terms. The experiments with use of the current neural machine
translation models are performed and its results are discussed. Ideas for
extending this line of research are proposed, and its relevance is motivated.
