计算机应用研究2025，Vol.42Issue(1)：214-221,8.DOI:10.19734/j.issn.1001-3695.2024.06.0208
基于语义理解增强的数学应用题机器解答方法
Machine solving method for math word problem based on semantic understanding enhancement
摘要
Abstract
Since the existing machine solving methods of math word problems cannot adaptively understand the text of the problem with changing semantics,and have a limit in the improvement of solving accuracy,this paper proposed a machine sol-ving method based on semantic understanding enhancement.Firstly,this method designed a semantically enhanced pre-train-ing language model SeBERT to accurately understand the topic through a multi-granularity knowledge modeling strategy and con-tinuous semantic integration strategy.Secondly,this method constructed the solution model SeBERT-PT,which adopted the solution structure of language model-pool-tree to effectively improve the semantic understanding deviation of word problems and the accuracy of understanding problems.Finally,it introduced a confidence-based judgment mechanism to directly deter-mine the failure of solving untrustworthy predictions,ensure the accuracy of the solution,and improve the training efficiency of solving models.The experimental results show that the accuracy results on Chinese and English datasets are 85.7％and 77.9％respectively,which is superior to other baseline methods,especially on problems involving complex semantic under-standing and logical reasoning.It has proved the effectiveness of the method in improving the accuracy of solving math word problems and demonstrates its wide applicability in cross-language environments.关键词
数学应用题求解/预训练语言模型/语义增强/池化/置信度Key words
math word problem solution/pre-trained language model/semantic enhancement/pooling/confidence分类
信息技术与安全科学引用本文复制引用
菅朋朋,闫鸣,王彦丽..基于语义理解增强的数学应用题机器解答方法[J].计算机应用研究,2025,42(1):214-221,8.基金项目
国家自然科学基金资助项目(62107014) （62107014）
河南省青年人才托举工程项目 (2023HYTP046) （2023HYTP046）
河南省重点研发与推广专项资助项目(232102320155) （232102320155）
河南省高等教育教学改革研究与实践重大项目(2021SJGLX017) （2021SJGLX017）
新工科背景下现代产业学院信创人才培养模式研究与实践(2024SJGLX0108) （2024SJGLX0108）
