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.

LEARNING AND ANALYZING VECTOR ENCODING OF SYMBOLIC REPRESENTATION

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LEARNING AND ANALYZING VECTOR ENCODING OF SYMBOLIC REPRESENTATION

Roland Fernandez, Aslı C¸elikyılmaz, Paul Smolensky, Rishabh Singh