Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints
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DOI：
10.18653/v1/2021.emnlp-main.484
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Zichao Wang;Andrew S. Lan;Richard Baraniuk
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文献类型：
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作者：
Zichao Wang;Andrew S. Lan;Richard Baraniuk
我们研究了生成算术数学应用题（MWPs）的问题，给出了指定数学计算的数学方程和指定问题场景的上下文。现有的方法容易产生数学上无效或语言质量不令人满意的MWP。他们也忽略了上下文或需要手动规范的问题模板，这损害了所生成的MWP的多样性。在本文中，我们开发了一种新的MWP生成方法，该方法利用i）预训练的语言模型和上下文关键字选择模型来提高生成的MWP的语言质量，ii）数学方程的方程一致性约束来提高生成的MWP的数学有效性。在三个真实世界的MWP数据集上进行的大量定量和定性实验表明，与各种基线相比，我们的方法具有上级性能。
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.