PatiGonit22K: A Comprehensive Dataset for Solving Complex Bengali MWPs PatiGonit22K：解决复杂孟加拉语数学应用题的全面数据集

TL;DR

• PatiGonit22K is a new Bengali Mathematical Word Problem (MWP) dataset containing 22,441 problems.
• It expands upon the original PatiGonit dataset by adding complex multi-operation equations alongside simple ones.
• The dataset was created through careful translation, annotation, cultural adaptation, and verification to ensure linguistic consistency and mathematical correctness.
• It addresses the lack of large-scale annotated resources for Bengali, facilitating research in quantitative reasoning and educational NLP for low-resource languages.

Why It Matters

This work is highly relevant as it tackles a significant gap in multilingual AI research. By providing a robust, high-quality benchmark for Bengali, PatiGonit22K enables the development and evaluation of models capable of understanding and solving math problems in a major low-resource language, thereby promoting inclusivity and advancing the state-of-the-art in cross-lingual mathematical reasoning.

Technical Details

• Dataset Size: Contains 22,441 distinct mathematical word problems.
• Content Variety: Includes both simple single-step equations and complex multi-operation equations to cover varying difficulty levels.
• Methodology: Problems were developed by extending an existing dataset (PatiGonit) with new content involving rigorous processes including translation, annotation, cultural adaptation, and verification.
• Goal: Designed specifically to serve as a balanced benchmark for evaluating natural language understanding and quantitative reasoning capabilities in Bengali.

Industry Insight

The release of PatiGonit22K signals a critical shift towards democratizing AI capabilities beyond dominant English-centric models. For practitioners, this highlights the strategic necessity of investing in localized datasets for emerging markets to build effective educational tools and reasoning systems. Future efforts should focus on leveraging such specialized benchmarks to fine-tune large language models for specific linguistic and cultural contexts, ensuring equitable access to advanced AI technologies globally.

摘要

PatiGonit22K是一个新的孟加拉语数学应用题（MWP）数据集，包含22,441个问题。
它在原始PatiGonit数据集的基础上进行了扩展，增加了复杂的多运算方程以及简单的方程。
该数据集通过仔细的翻译、标注、文化适应和验证来创建，以确保语言一致性和数学正确性。
它解决了孟加拉语缺乏大规模标注资源的问题，促进了低资源语言在定量推理和教育自然语言处理方面的研究。

深度分析

TL;DR

• PatiGonit22K是一个新的孟加拉语数学应用题（MWP）数据集，包含22,441个问题。
• 它在原始PatiGonit数据集的基础上进行了扩展，增加了复杂的多运算方程以及简单的方程。
• 该数据集通过仔细的翻译、标注、文化适应和验证来创建，以确保语言一致性和数学正确性。
• 它解决了孟加拉语缺乏大规模标注资源的问题，促进了低资源语言在定量推理和教育自然语言处理方面的研究。

为什么重要

这项工作非常重要，因为它解决了多语言AI研究中的一个重大差距。通过为孟加拉语提供一个稳健且高质量的基准，PatiGonit22K使得能够开发和评估能够理解和解决主要低资源语言中数学问题的模型，从而促进包容性并推进跨语言数学推理的前沿技术。

技术细节

• 数据集大小： 包含22,441个不同的数学应用题。
• 内容多样性： 包括简单的单步方程和复杂的多运算方程，以覆盖不同的难度级别。
• 方法论： 问题是通过扩展现有数据集（PatiGonit）并使用涉及严格过程的新内容开发的，包括翻译、标注、文化适应和验证。
• 目标： 专门设计用作评估孟加拉语自然语言理解和定量推理能力的平衡基准。

行业见解

PatiGonit22K的发布标志着向民主化AI能力的重要转变，超越了主导的英语为中心模型。对于从业者来说，这突出了投资新兴市场的本地化数据集的战略必要性，以构建有效的教育工具和推理系统。未来 Eff