2021/09/15 by Maria Ryskina, Kevin Knight, Ryskina, Maria +1 · 1 voice
#cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.2109.07230
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.