# BMWP: the first Bengali math word problems dataset for operation prediction and solving

## TL;DR

This paper introduces the first Bengali math word problem dataset, BMWP, to evaluate AI performance in solving problems in a low-resource language.

## Contribution

The paper presents BMWP, the first benchmark dataset for Bengali math word problems, enabling operation prediction and problem-solving in a low-resource language.

## Key findings

- BMWP contains 8653 Bengali math word problems for benchmarking.
- State-of-the-art deep learning models achieved 92±2% accuracy in operation prediction.
- The dataset and code are publicly available for research use.

## Abstract

Solving math word problems of varying complexities is one of the most challenging and exciting research questions in artificial intelligence (AI), particularly in natural language processing (NLP) and machine learning (ML). Foundational language models such as GPT must be evaluated for intelligence, and solving word problems is a key method for this assessment. These problems become especially difficult when presented in low-resource regional languages such as Bengali. Word problem solving integrates the cognitive domains of language processing, comprehension, and transformation into real-world solutions. During the past decade, advances in AI and machine learning have significantly progressed in addressing this complex issue. Although researchers worldwide have primarily utilized datasets in English and some in Chinese, there has been a lack of standard datasets for low-resource languages such as Bengali. In this pioneering study, we introduce the first Bengali Math Word Problem Benchmark Data Set (BMWP), comprising 8653 word problems. We detail the creation of this dataset and the benchmarking methods employed. Furthermore, we investigate operation prediction from Bengali word problems using state-of-the-art deep learning (DL) techniques. We implemented and compared various standard DL-based neural network architectures, achieving an accuracy of \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$92 \pm 2\%$$\end{document}92±2%. The data set and the code will be available at https://github.com/SanchitaMondal/BMWP.