Empowering Bengali Education with AI: Solving Bengali Math Word Problems through Transformer Models
This work addresses a challenge in natural language processing for low-resource languages like Bengali, offering incremental improvements for educational AI tools.
The paper tackled solving Bengali math word problems by developing transformer-based models and introducing the 'PatiGonit' dataset of 10,000 problems, with the mT5 model achieving 97.30% accuracy.
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.