Learning and analyzing vector encoding of symbolic representations

We present a formal language with expressions denoting general symbol
structures and queries which access information in those structures. A
sequence-to-sequence network processing this language learns to encode symbol
structures and query them. The learned representation (approximately) shares a
simple linearity property with theoretical techniques for performing this task.