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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。
Updated 1 year ago Text
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Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
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