Title: Discrete Word Embedding for Logical Natural Language Understanding. Abstract: We propose an unsupervised neural model for learning a discrete embedding of words.
Unlike existing discrete embeddings, our binary embedding supports vector arithmetic operations similar to continuous embeddings.
Our embedding represents each word as a set of propositional statements describing a transition rule in classical/STRIPS planning formalism.
This makes the embedding directly compatible with symbolic, state of the art classical planning solvers.