BMWP: the first Bengali math word problems dataset for operation prediction and solving.

Discover artificial intelligence Pub Date : 2025-01-01 Epub Date: 2025-03-13 DOI:10.1007/s44163-025-00243-7
Sanchita Mondal, Debnarayan Khatua, Sourav Mandal, Dilip K Prasad, Arif Ahmed Sekh
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Abstract

Solving math word problems of varying complexities is one of the most challenging and exciting research questions in artificial intelligence (AI), particularly in natural language processing (NLP) and machine learning (ML). Foundational language models such as GPT must be evaluated for intelligence, and solving word problems is a key method for this assessment. These problems become especially difficult when presented in low-resource regional languages such as Bengali. Word problem solving integrates the cognitive domains of language processing, comprehension, and transformation into real-world solutions. During the past decade, advances in AI and machine learning have significantly progressed in addressing this complex issue. Although researchers worldwide have primarily utilized datasets in English and some in Chinese, there has been a lack of standard datasets for low-resource languages such as Bengali. In this pioneering study, we introduce the first Bengali Math Word Problem Benchmark Data Set (BMWP), comprising 8653 word problems. We detail the creation of this dataset and the benchmarking methods employed. Furthermore, we investigate operation prediction from Bengali word problems using state-of-the-art deep learning (DL) techniques. We implemented and compared various standard DL-based neural network architectures, achieving an accuracy of 92 ± 2 % . The data set and the code will be available at https://github.com/SanchitaMondal/BMWP.

BMWP:第一个用于操作预测和解决的孟加拉数学字题数据集。
解决不同复杂性的数学单词问题是人工智能(AI)中最具挑战性和最令人兴奋的研究问题之一,特别是在自然语言处理(NLP)和机器学习(ML)中。像GPT这样的基础语言模型必须对智力进行评估,而解决单词问题是这项评估的关键方法。当使用诸如孟加拉语等资源匮乏的地区性语言时,这些问题变得尤其困难。解决文字问题将语言处理、理解和转换的认知领域整合到现实世界的解决方案中。在过去十年中,人工智能和机器学习的进步在解决这一复杂问题方面取得了重大进展。虽然世界各地的研究人员主要使用英语和一些中文数据集,但缺乏针对孟加拉语等低资源语言的标准数据集。在这项开创性的研究中,我们引入了第一个孟加拉数学字题基准数据集(BMWP),包括8653个字题。我们详细介绍了该数据集的创建和所采用的基准测试方法。此外,我们使用最先进的深度学习(DL)技术研究了孟加拉语单词问题的操作预测。我们实现并比较了各种标准的基于dl的神经网络架构,达到了92±2%的精度。数据集和代码可在https://github.com/SanchitaMondal/BMWP上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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