A machine solution for math word problems based on semantic understanding enhancement

The adaptive understanding of problem text with various semantics is challenging for machines when solving math word problems. This challenge is particularly important in the context of intelligent technology promoting equitable and sustainable development in education. Most existing methods focus on numerical information processing and ignore important semantic information, which leads to limited improvement of solution accuracy. Therefore, this study proposes a machine solution based on semantic understanding enhancement. First, a knowledge-enhanced pre-trained language model is constructed as a semantic encoder to integrate background information such as phrases and entities to enhance the understanding of lexical, syntactic, and semantic issues. Then, pooling operations are introduced to improve the semantic understanding further, and a readable binary expression tree is generated using a tree structure decoder. Finally, a judgment mechanism based on confidence is proposed to ensure the accuracy of the solution and improve the training efficiency. The experiments show that this method is superior to other baselines on both Chinese and English datasets, which proves its effectiveness and feasibility. This result not only provides new ideas and methods for mathematical solutions but also creates new possibilities for the combination of intelligence and sustainable education.