Math Word Problem (MWP) solving is a critical task in natural language processing, has garnered significant research interest in recent years. Various recent studies heavily rely on Seq2Seq models and their extensions (e.g., Seq2Tree and Graph2Tree) to generate mathematical equations. While effective, these models struggle to generate diverse but counterpart solution equations, limiting their generalization across various math problem scenarios. In this paper, we introduce a novel Diversity-enhanced Knowledge Distillation (DivKD) model for practical MWP solving. Our approach proposes an adaptive diversity distillation method, in which a student model learns diverse equations by selectively transferring high-quality knowledge from a teacher model. Additionally, we design a diversity prior-enhanced student model to better capture the diversity distribution of equations by incorporating a conditional variational auto-encoder. Extensive experiments on {four} MWP benchmark datasets demonstrate that our approach achieves higher answer accuracy than strong baselines while maintaining high efficiency for practical applications.

本研究针对数学文字问题解决中的多样性不足问题，提出了一种创新的多样性增强知识蒸馏（DivKD）模型。该模型通过自适应多样性蒸馏方法，使学生模型能够选择性地从教师模型转移高质量知识，从而生成更为多样化的数学方程。实验结果表明，该方法在多个基准数据集上均优于现有强基线，表现出更高的解答准确率和实用效率。

实用数学文字问题解决的多样性增强知识蒸馏模型