Predicting the Types of Formulas in a Mathematical Problem Text

Abstract

We present a system that predicts the types of formulas in a math text using a neural language model and type inference. Firstly, we enumerate possible types of a formula with type inference. Since a formula generally has multiple interpretations, we cannot fully determine its type without the context. Secondly, we input the formulas and the context into a neural language model and predict their types with certainty scores. Finally, we select the type which obtained the highest score for each formula. Experimental results on a math problem dataset show that, unfortunately, the accuracy of the prediction deteriorates when we combine symbolic type inference with statistical prediction.