基于AI深度学习的初等数学应用题分析

Mingzhe Li
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引用次数: 1

摘要

:自然语言处理(NLP)在机器学习方面取得了很大进展,但数学教育软件缺乏AI集成来解决英语数学单词问题。我们建议使用BertGen预训练的Transformer模型,以及由我们的数据集增强器增强的MAWPS数据集。Transformer模型具有多头注意机制,擅长捕捉远程依赖关系和参考关系,这对于小学水平的数学单词问题至关重要。我们在不同数据集上的准确性测试和性能验证了我们方法的有效性和可泛化性。此外,我们的增强数据集优于较小的未增强数据集,同时保持了多样性。数学单词问题增强器可以用于其他数学问题集,支持该领域的未来研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Elementary Math Word Problems Based on AI Deep Learning
: Natural language processing (NLP) has greatly advanced in machine learning, but math education software lacks AI integration for solving math word problems in English. We propose using the BertGen pre-trained Transformer model, along with the MAWPS dataset augmented by our dataset augmenter. The Transformer model, with its multi-head attention mechanisms, excels at capturing long-range dependencies and referential relationships, crucial for math word problems at the primary school level. Our accuracy tests and performance on different datasets validate the effectiveness and generalizability of our approach. Moreover, our augmented dataset outperforms smaller unaugmented datasets, while maintaining diversity. The math word problem augmenter can be adapted for other math problem sets, supporting future research in the field.
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