构建2.2万道复杂孟加拉语数学题数据集，推动低资源语言数学推理研究。
PatiGonit22K: A Comprehensive Dataset for Solving Complex Bengali MWPs
- 扩展原数据集，新增22,441道孟加拉语数学应用题。
- 涵盖单步与多步运算，覆盖不同难度层级的数学推理任务。
- 专为低资源语言教育NLP研究设计，适合数学推理与跨语言研究者使用。
数学应用题（MWPs）是评估自然语言理解与定量推理能力的重要基准。尽管高资源语言已取得进展，孟加拉语因大规模标注数据稀缺而研究不足。本文推出PatiGonit22K，一个包含22,441道题目的扩展孟加拉语数学应用题数据集，通过大幅扩充原PatiGonit数据集中的复杂数学问题构建而成。该数据集包含简单与多步运算方程，提供覆盖不同难度水平的均衡评测基准。每道题目均经过精心翻译、标注、文化适配与验证，确保语言一致性与数学正确性。通过提升数据规模与复杂度，PatiGonit22K为低资源语言的数学推理与教育NLP研究提供了更全面的资源支持。
原文摘要 · Abstract (English)
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.
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