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Learning Mathematical Properties of Integers

2021-09-15EMNLP (BlackboxNLP) 2021Code Available0· sign in to hype

Maria Ryskina, Kevin Knight

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Abstract

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.

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