Sunday 02 March 2025
A team of researchers has made a significant breakthrough in the field of natural language processing, developing an innovative approach to solving Bengali math word problems using transformer-based models. The study, published in a recent scientific paper, demonstrates the effectiveness of these models in accurately translating complex Bengali word problems into mathematical equations.
Math word problems are a fundamental part of elementary school education, but they can be challenging for students who don’t speak English as their first language. To address this issue, researchers have been working on developing artificial intelligence (AI) systems that can solve math word problems. However, most of these systems have been designed to work with English language inputs and may not perform well with other languages.
The Bengali language is a low-resource language, meaning it has limited computational resources and datasets available for training AI models. This lack of data makes it challenging for researchers to develop AI systems that can accurately process Bengali text. However, the new study demonstrates that transformer-based models can be successfully fine-tuned to handle Bengali math word problems.
The researchers created a dataset of 10,000 Bengali math word problems and used it to train four different transformer-based models: Basic Transformer, mT5, BanglaT5, and mBART50. The models were designed to identify the specific words and phrases in the text that correspond to mathematical operations, such as addition and subtraction.
The results showed that the mT5 model achieved the highest accuracy of 97.30%, followed closely by mBART50 with an accuracy of 97.20%. The other two models, Basic Transformer and BanglaT5, performed less well but still demonstrated significant improvements over traditional deep learning methods.
The study’s findings have important implications for education in Bengali-speaking countries. By developing AI systems that can accurately solve math word problems in Bengali, researchers can create more effective educational tools that cater to the needs of students who speak different languages.
The paper also highlights the potential applications of transformer-based models beyond education. These models can be used to develop chatbots and virtual assistants that can understand and respond to natural language inputs in various languages.
Overall, the study demonstrates the power of transformer-based models in processing Bengali text and solving math word problems. The results have significant implications for education and AI research, and highlight the potential for these models to be applied in a wide range of applications.
Cite this article: “Transformer-Based Models Accurately Solve Bengali Math Word Problems”, The Science Archive, 2025.
Bengali, Natural Language Processing, Math Word Problems, Transformer-Based Models, Ai, Education, Language Translation, Machine Learning, Deep Learning, Nlp.
