Donggeun Kim  Nayeon Lee  Hyunwoo Sim  Myoung-Wan Koo

10.5626/JOK.2022.49.10.807

math word problem mathematical relationship extraction graph-to-tree learning

This paper builds its own data set of eight types of Korean math word problems and presents a Ko-Graph2Tree model, an automatic solution model for Korean math word problems based on Graph2Tree model not previously presented. The recently released Graph2Tree model is a graph-to-tree learning based model that shows better performance than existing natural language processing models for automatic solving English math word problems. Using two types of graphs reflecting the relationship and order between numbers in the problem text, that is, mathematical relations, in solution generation, the model showed improved performance compared to existing tree-based models. As a result of measuring performance after learning with a self-produced Korean math word problem dataset, the transformer model with sequence-to-sequence structure showed an accuracy of 42.3%, whereas the Ko-Graph2Tree model showed an accuracy of 68.3%, resulting in 26.0%p higher performance.