Empowering Bengali Education with AI: Solving Bengali Math Word Problems through Transformer Models

TL;DR

This paper presents a transformer-based approach for solving Bengali math word problems, introducing a new dataset and achieving high accuracy, thereby advancing NLP and educational tools for low-resource languages.

Contribution

It introduces the PatiGonit dataset and fine-tunes transformer models to effectively solve Bengali MWPs, a novel approach for low-resource language NLP tasks.

Findings

mT5 achieved 97.30% accuracy in solving Bengali MWPs

Transformer models effectively translate Bengali word problems into equations

The study advances NLP resources and methods for Bengali educational applications

Abstract

Mathematical word problems (MWPs) involve the task of converting textual descriptions into mathematical equations. This poses a significant challenge in natural language processing, particularly for low-resource languages such as Bengali. This paper addresses this challenge by developing an innovative approach to solving Bengali MWPs using transformer-based models, including Basic Transformer, mT5, BanglaT5, and mBART50. To support this effort, the "PatiGonit" dataset was introduced, containing 10,000 Bengali math problems, and these models were fine-tuned to translate the word problems into equations accurately. The evaluation revealed that the mT5 model achieved the highest accuracy of 97.30%, demonstrating the effectiveness of transformer models in this domain. This research marks a significant step forward in Bengali natural language processing, offering valuable methodologies and resources for educational AI tools. By improving math education, it also supports the development of advanced problem-solving skills for Bengali-speaking students.