BMWP:第一个孟加拉语数学词题数据集,用于操作预测和解决
Sanchita Mondal1, Debnarayan Khatua2, Sourav Mandal3
1School of Medical Science and Technology, IIT Kharagpur, Kharagpur, West Bengal 721302 India.
Discover artificial intelligence
|March 17, 2025
概括
本研究介绍了第一个孟加拉语数学词语问题基准数据集 (BMWP) 用于人工智能 (AI) 研究. 它对解决孟加拉语数学单词问题的深度学习模型进行了基准测试,为低资源语言推进NLP.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 解决数学单词问题是人工智能和语言模型智能的关键基准.
- 像孟加拉语这样的低资源语言缺乏用于此任务的标准化数据集.
- 现有的研究主要使用英语和中文数据集.
研究的目的:
- 推出第一个孟加拉语数学单词问题基准数据集 (BMWP).
- 建立对孟加拉语单词问题的AI模型评估的基准方法.
- 研究深度学习模型在解决这些问题的表现.
主要方法:
- 创建一个包含8653个词题的BMWP数据集.
- 实现和比较最先进的深度学习神经网络架构.
- 从孟加拉语单词问题对操作预测的基准性能.
主要成果:
- BMWP数据集为低资源语言NLP研究提供了一个新的资源.
- 深度学习模型在解决孟加拉语数学单词问题的有效性方面得到了评估.
- 在操作预测中达到[具体准确值,如果可用,否则说明'有希望的结果']的准确性.
结论:
- 开发的BMWP数据集解决了孟加拉语NLP资源的关键缺口.
- 这项研究表明,应用深度学习来解决孟加拉语数学单词问题的可行性.
- 数据集和代码将公开提供,以促进进一步的研究.
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