计算机工程2026，Vol.52Issue(5)：95-102,8.DOI:10.19678/j.issn.1000-3428.0070131
基于增强数值表示的数学应用题求解模型研究
Research on Math Word Problem Solving Model Based on Enhanced Numerical Representation
摘要
Abstract
The automatic resolution of Math Word Problems(MWP)is a current research hotspot in the academic community.Despite the significant progress made in the existing research,most studies treat numerical values in MWPs as placeholders and simply process them as ordinary text,overlooking the importance of numerical semantics in solving MWPs.To address this issue,this study proposes an enhanced numerical representation model for MWP solving based on the generic"encoder-decoder"architecture.The model achieves this by utilizing Graph Convolutional Neural Networks(GCNN)to explicitly model the semantic relationships between numerical values and between numerical values and context text and introducing auxiliary learning tasks to guide the model to fully capture task-related numerical semantics.This significantly enhances the numerical modeling capability of the encoder.Empirical evidence from the commonly used MWP datasets,Math23K and MAWPS,shows that the proposed model can fully consider numerical semantics and outperform mainstream classical models in solving large-scale Chinese application problem sets.关键词
数学应用题/图卷积神经网络/辅助学习任务/图编码层/图变换层Key words
Math Word Problems(MWP)/Graph Convolutional Neural Networks(GCNN)/auxiliary learning tasks/graph encoding layer/graph transformation layer分类
信息技术与安全科学引用本文复制引用
胡静丹,李波,杨静..基于增强数值表示的数学应用题求解模型研究[J].计算机工程,2026,52(5):95-102,8.基金项目
教育部人文社会科学研究青年基金(19YJC870012). （19YJC870012）
