Solving arithmetic word problems: A deep learning based approach

Issue title: Special section: Selected papers of LKE 2019

Guest editors: David Pinto, Vivek Singh and Fernando Perez

Article type: Research Article

Authors: Mandal, Sourav^{a; c; *} | Sekh, Arif Ahmed^b | Naskar, Sudip Kumar^a

Affiliations: [a] Department of Computer Science & Engineering, Jadavpur University, Kolkata, India | [b] Department of Physics and Technology, UiT The Arctic University of Norway, Norway | [c] Department of Computer Science & Engineering, Haldia Institute of Technology, Haldia, India

Correspondence: [*] Corresponding author. Sourav Mandal, E-mail: [email protected] .

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.

Keywords: Solving arithmetic word problems, solving math word problems, BiLSTM-based operation prediction, irrelevant information removal

DOI: 10.3233/JIFS-179911

Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2521-2531, 2020