Distilling Wikipedia mathematical knowledge into neural network models

Machine learning applications to symbolic mathematics are becoming increasingly popular, yet there lacks a centralized source of real-world symbolic expressions to be used as training data. In contrast, the field of natural language processing leverages resources like Wikipedia that provide enormous amounts of realworld textual data. Adopting the philosophy of "mathematics as language," we bridge this gap by introducing a pipeline for distilling mathematical expressions embedded in Wikipedia into symbolic encodings to be used in downstream machine learning tasks. We demonstrate that a mathematical language model trained on this "corpus" of expressions can be used as a prior to improve the performance of neural-guided search for the task of symbolic regression. "The basis of all human culture is language, and mathematics is a special kind of linguistic activity."