Math Word Problem Solver Based on Text-to-Text Transformer Model

In recent years, automatic problem solving for mathematical words has attracted increasing attention. Therefore, algorithms developed for solving mathematical word problems like humans are a key technology to facilitate the development of digital education. In this paper, a deep learning model based on a text-to-text conversion model is proposed to solve mathematical word problems. The deep learning model treats each mathematical word problem as a "text-to-text" problem, i.e. taking a text as input and producing a new text as output. However, the output text appears in the form of a mathematical expression. In our experiments, this paper evaluates a deep learning model of Ape210K, which consists of 210K Chinese primary school level maths problems. The experimental results show that our deep learning model presented in this paper can solve 78.61% of the mathematical word problems as a whole. In addition, the problems were classified into six categories based on the knowledge points involved, and this paper explores the model's performance on different types of problems.