A Multi-language Platform for Generating Algebraic Mathematical Word Problems

TL;DR

This paper introduces a deep learning-based multi-language platform that generates algebraic mathematical word problems in English and Sinhala, overcoming template limitations with high accuracy.

Contribution

It presents a novel character-level LSTM model combined with POS tagging to generate customizable math word problems in multiple languages.

Findings

Achieves over 90% accuracy in problem generation

Supports both English and Sinhala languages

Demonstrates improved flexibility over template-based methods

Abstract

Existing approaches for automatically generating mathematical word problems are deprived of customizability and creativity due to the inherent nature of template-based mechanisms they employ. We present a solution to this problem with the use of deep neural language generation mechanisms. Our approach uses a Character Level Long Short Term Memory Network (LSTM) to generate word problems, and uses POS (Part of Speech) tags to resolve the constraints found in the generated problems. Our approach is capable of generating Mathematics Word Problems in both English and Sinhala languages with an accuracy over 90%.