Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints
Zichao Wang, Andrew Lan, Richard Baraniuk
Abstract
We study the problem of generating arithmetic math word problems (MWPs) given a math equation that specifies the mathematical computation and a context that specifies the problem scenario. Existing approaches are prone to generating MWPs that are either mathematically invalid or have unsatisfactory language quality. They also either ignore the context or require manual specification of a problem template, which compromises the diversity of the generated MWPs. In this paper, we develop a novel MWP generation approach that leverages i) pre-trained language models and a context keyword selection model to improve the language quality of generated MWPs and ii) an equation consistency constraint for math equations to improve the mathematical validity of the generated MWPs. Extensive quantitative and qualitative experiments on three real-world MWP datasets demonstrate the superior performance of our approach compared to various baselines.
BibTeX
@inproceedings{wang-etal-2021-math,
title = "Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints",
author = "Wang, Zichao and
Lan, Andrew and
Baraniuk, Richard",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.484/",
doi = "10.18653/v1/2021.emnlp-main.484",
pages = "5986--5999"
}