Learning by Analogy: Diverse Questions Generation in Math Word Problem
Zihao Zhou Maizhen Ning Qiufeng Wang Jie Yao Wei Wang Xiaowei Huang Kaizhu Huang
Solving math word problem (MWP) with AI techniques has recently made great
progress with the success of deep neural networks (DNN), but it is far from
being solved. We argue that the ability of learning by analogy is essential for
an MWP solver to better understand same problems which may typically be
formulated in diverse ways. However most existing works exploit the shortcut
learning to train MWP solvers simply based on samples with a single question.
In lack of diverse questions, these methods merely learn shallow heuristics. In
this paper, we make a first attempt to solve MWPs by generating diverse yet
consistent questions/equations. Given a typical MWP including the scenario
description, question, and equation (i.e., answer), we first generate multiple
consistent equations via a group of heuristic rules. We then feed them to a
question generator together with the scenario to obtain the corresponding
diverse questions, forming a new MWP with a variety of questions and equations.
Finally we engage a data filter to remove those unreasonable MWPs, keeping the
high-quality augmented ones. To evaluate the ability of learning by analogy for
an MWP solver, we generate a new MWP dataset (called DiverseMath23K) with
diverse questions by extending the current benchmark Math23K. Extensive
experimental results demonstrate that our proposed method can generate
high-quality diverse questions with corresponding equations, further leading to
performance improvement on Diverse-Math23K. The code and dataset is available
at: https://github.com/zhouzihao501/DiverseMWP