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 the 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.

本文提出了一种新颖的数学语文问题生成方法，该方法结合了预训练的语言模型和上下文关键词选择模型，以提高所生成的数学语文问题的语言质量；而使用数学公式一致性约束来提高所生成的数学语文问题的数学有效性。经过大量的定量和定性实验，我们的方法相比各种基线方法都具有更好的性能。