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Academic Journal of Mathematical Sciences, 2023, 4(3); doi: 10.25236/AJMS.2023.040310.

Analysis of Elementary Math Word Problems Based on AI Deep Learning


Mingzhe Li

Corresponding Author:
Mingzhe Li

Deerfield Academy, Franklin County, Massachusetts, United States of America


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.


Math word problem, Natural language processing, Transformer model

Cite This Paper

Mingzhe Li. Analysis of Elementary Math Word Problems Based on AI Deep Learning. Academic Journal of Mathematical Sciences (2023) Vol. 4, Issue 3: 60-69. https://doi.org/10.25236/AJMS.2023.040310.


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