Discrete Word Embedding for Logical Natural Language Understanding

• URL: http://arxiv.org/abs/2008.11649v2
• Date: Thu, 15 Oct 2020 14:37:59 GMT
• Title: Discrete Word Embedding for Logical Natural Language Understanding
• Authors: Masataro Asai, Zilu Tang
• Abstract summary: We propose an unsupervised neural model for learning a discrete embedding of words. Our embedding represents each word as a set of propositional statements describing a transition rule in classical/STRIPS planning formalism.
• Score: 5.8088738147746914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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