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.

翻译：高维矢量空间的嵌入文字在许多自然语言应用中被证明是有价值的。 在这项工作中,我们调查通过经过类似训练的整数嵌入是否能够捕捉到对数学应用有用的概念。 我们探索数学知识的整数嵌入,将其应用到一组数字推理任务中,并表明通过从数学序列数据中学习表达方式,我们可以大大改进从英文文本组合中学习的数字嵌入。