Embedding words in high-dimensional vector spaces has proven valuable in many natural language applications. In this work, we investigate whether similarly-trained embeddings of integers can capture concepts that are useful for mathematical applications. We probe the integer embeddings for mathematical knowledge, apply them to a set of numerical reasoning tasks, and show that by learning the representations from mathematical sequence data, we can substantially improve over number embeddings learned from English text corpora.
@article{arxiv.2109.07230,
title = {Learning Mathematical Properties of Integers},
author = {Maria Ryskina and Kevin Knight},
journal= {arXiv preprint arXiv:2109.07230},
year = {2021}
}