用Transformer模型解决孟加拉语数学应用题，提升本土教育AI能力
Empowering Bengali Education with AI: Solving Bengali Math Word Problems through Transformer Models
- 基于mT5等Transformer模型，将孟加拉语题干转为数学方程
- 在1万条数据上，mT5模型准确率达97.30%，表现最优
- 为低资源语言教育AI提供可复用的数据与方法
数学应用题（MWPs）要求将文本描述转化为数学方程，这对自然语言处理构成挑战，尤其在孟加拉语等低资源语言中。本文提出一种基于Transformer模型的创新方法，包括Basic Transformer、mT5、BanglaT5和mBART50，用于求解孟加拉语数学应用题。为此构建了包含10,000个孟加拉语数学问题的“PatiGonit”数据集，并对上述模型进行微调，以实现题干到方程的高精度转换。评估结果显示，mT5模型达到最高准确率97.30%，证明了Transformer模型在该任务中的有效性。本研究推动了孟加拉语自然语言处理的发展，为教育类AI工具提供了宝贵的方法论与资源，有助于提升孟加拉语学生的数学教育水平与问题解决能力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。