Learning Mathematical Properties of Integers
This work addresses the challenge of enhancing numerical reasoning in AI systems, though it appears incremental as it builds on existing embedding methods.
The paper tackled the problem of whether integer embeddings can capture mathematical concepts by training them on mathematical sequence data, resulting in substantial improvements over embeddings learned from English text corpora for numerical reasoning tasks.
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.