发表机构
Ahsanullah University of Science and Technology; Southeast University（阿山努拉科技大学; 东南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对孟加拉语数学应用题因标注数据集有限未充分探索的问题，通过扩展原始数据集开发了含22441个问题的PatiGonit22K数据集，涵盖不同难度方程，经多步骤处理确保质量，为低资源语言相关研究提供了全面资源。
AI 中文摘要
数学应用题是评估自然语言理解和定量推理的重要基准。尽管高资源语言近期取得进展，但由于大规模标注数据集有限，孟加拉语仍未得到充分探索。本文介绍了PatiGonit22K，这是一个扩展的孟加拉语数学应用题数据集，包含22441个问题，通过扩展原始数据集并加入大量复杂数学问题而开发。该数据集包括简单和多步运算方程，为评估不同难度级别的数学推理提供了平衡基准。每个问题都经过仔细翻译、标注、文化适配和验证，以确保语言一致性和数学正确性。PatiGonit22K通过增加孟加拉语数学应用题的规模和复杂性，为低资源语言中数学推理和教育自然语言处理应用的未来研究提供了更全面的资源。
英文摘要
Mathematical Word Problems (MWPs) are an important benchmark for evaluating natural language understanding and quantitative reasoning. Despite recent progress in high resource languages, Bengali remains underexplored due to the limited availability of large scale annotated datasets. In this work, we introduce PatiGonit22K, an expanded Bengali MWP dataset containing 22,441 problems, developed by extending the original PatiGonit dataset with a substantially larger collection of complex mathematical problems. The dataset includes both simple and multi operation equations, providing a balanced benchmark for evaluating mathematical reasoning across different difficulty levels. Each problem is carefully translated, annotated, culturally adapted, and verified to ensure linguistic consistency and mathematical correctness. By increasing both the scale and complexity of Bengali MWPs, PatiGonit22K provides a more comprehensive resource for future research on mathematical reasoning and educational NLP applications in low resource languages.
