Distilling Wikipedia mathematical knowledge into neural network models

TL;DR

This paper introduces a pipeline to extract and encode mathematical expressions from Wikipedia, creating a valuable resource for training machine learning models in symbolic mathematics, and demonstrates its effectiveness in symbolic regression tasks.

Contribution

It presents a novel method for distilling Wikipedia's mathematical content into symbolic encodings, enabling improved neural-guided symbolic regression.

Findings

Enhanced performance in symbolic regression tasks

Effective encoding of Wikipedia's mathematical expressions

A new resource for symbolic mathematics machine learning

Abstract

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 real-world 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 $\textit{mathematical}$ $\textit{language}$ $\textit{model}$ 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.