Solving arithmetic word problems: A deep learning based approach

Abstract

This paper presents a novel deep learning based approach to solving arithmetic word problems. Solving different types of mathematical (math) word problems (MWP) is a very complex and challenging task as it requires Natural Language Understanding (NLU) and Commonsense knowledge. An application on this can benefit learning (education) technologies such as E-learning systems, Intelligent tutoring, Learning Management Systems (LMS), Innovative teaching/learning, etc. We propose Deep Learning based Arithmetic Word Problem Solver, DLAWPS, an intelligent MWP solver system. DLAWPS consists of a Recurrent Neural Network (RNN) based Bi-directional Long Short-Term Memory (BiLSTM) to classify operation among four basic operations {+ , - , * , /}, and a knowledge-based irrelevant information removal unit (IIRU) to identify the relevant quantities to form an equation to solve arithmetic MWPs. Our system generates state-of-the-art results on the standard arithmetic word problem datasets –AddSub, SingleOp, and a Combined dataset.