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该算法提出一个端到端的神经网络模型，可以利用常识知识图谱和方程表达式生成丰富的数学应用题（MWP）。所提出的模型（1）从符号方程表达式和常识知识的 Levi 图中学习表征；（2）在生成MWP 时，通过自我规划模块自动融合方程式和常识知识信息。在教育场景数据集的实验表明，和其它模型相比，该方法在 MWP 生成任务上有明显优势，在自动评价指标和人类评价指标方面都优于 SOTA 模型。在教育数据集中，方程相关性分数达到 2.308，主题相关性分数达到 2.558，语言连贯性分数达到 2.505。

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