- Abstract: 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.
- Keywords: Knowledge, Representation, Embedding, Symbolic Structure, Encoder Decoder, Deep Learning, Vector Space
- TL;DR: We present an expression language for representing and querying structured symbolic information and use it to train an encoder decoder network to represent the expressions as fixed size decodable embedding vectors.