# Discrete Word Embedding for Logical Natural Language Understanding

arXiv: https://arxiv.org/abs/2008.11649 · HTML: https://mlsanity.com/paper/2008.11649/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

## Built on by (most cited first, 1 of 1)

- [Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents](https://mlsanity.com/paper/2106.05387/) (2021)

## Abstract (arXiv)

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.