Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints
Zichao
Wang
author
Andrew
Lan
author
Richard
Baraniuk
author
2021-11
text
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Marie-Francine
Moens
editor
Xuanjing
Huang
editor
Lucia
Specia
editor
Scott
Wen-tau
Yih
editor
Association for Computational Linguistics
Online and Punta Cana, Dominican Republic
conference publication
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.
wang-etal-2021-math
10.18653/v1/2021.emnlp-main.484
https://aclanthology.org/2021.emnlp-main.484
2021-11
5986
5999