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arXiv · 2008.11649

Discrete Word Embedding for Logical Natural Language Understanding

Masataro Asai·Zilu Tang
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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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Masataro Asai, Zilu Tang. 2020-08-26. Discrete Word Embedding for Logical Natural Language Understanding. https://arxiv.org/abs/2008.11649

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