用AI赋能孟加拉语教育：通过Transformer模型求解孟加拉语数学应用题
Empowering Bengali Education with AI: Solving Bengali Math Word Problems through Transformer Models
AI总结：
本文构建含10,000道题的PatiGonit数据集，并微调多种Transformer模型求解孟加拉语数学应用题，其中mT5以97.30%准确率取得最佳效果。
摘要：
数学应用题（MWP）涉及将文本描述转换为数学方程的任务。这在自然语言处理中构成重大挑战，尤其是对于孟加拉语等低资源语言。本文通过开发一种创新方法来应对这一挑战，该方法使用基于Transformer的模型求解孟加拉语MWP，包括Basic Transformer、mT5、BanglaT5和mBART50。为支持这项工作，论文引入了“PatiGonit”数据集，其中包含10,000道孟加拉语数学题，并对这些模型进行微调，以准确地将应用题翻译为方程。评估显示，mT5模型取得了97.30%的最高准确率，证明了Transformer模型在该领域的有效性。本研究标志着孟加拉语自然语言处理的重要进步，为教育AI工具提供了有价值的方法和资源。通过改善数学教育，它还支持孟加拉语学生发展高级问题解决技能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
