  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.
