LEARNING AND ANALYZING VECTOR ENCODING OF SYMBOLIC REPRESENTATION

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

Feb 12, 2018 (modified: Jun 04, 2018) ICLR 2018 Workshop Submission readers: everyone Show Bibtex
  • 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.
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