Heterogeneous Line Graph Transformer for Math Word Problems
This work addresses the need for more intelligent online learning systems by improving automated solvers for tasks like homework correction, but it is incremental as it builds on existing graph-based methods with added heterogeneity.
The authors tackled the problem of automated math word problem solving by proposing a heterogeneous line graph transformer (HLGT) model that leverages relationships between token types like entities and numbers, achieving better performance than existing models, though still below human levels.
This paper describes the design and implementation of a new machine learning model for online learning systems. We aim at improving the intelligent level of the systems by enabling an automated math word problem solver which can support a wide range of functions such as homework correction, difficulty estimation, and priority recommendation. We originally planned to employ existing models but realized that they processed a math word problem as a sequence or a homogeneous graph of tokens. Relationships between the multiple types of tokens such as entity, unit, rate, and number were ignored. We decided to design and implement a novel model to use such relational data to bridge the information gap between human-readable language and machine-understandable logical form. We propose a heterogeneous line graph transformer (HLGT) model that constructs a heterogeneous line graph via semantic role labeling on math word problems and then perform node representation learning aware of edge types. We add numerical comparison as an auxiliary task to improve model training for real-world use. Experimental results show that the proposed model achieves a better performance than existing models and suggest that it is still far below human performance. Information utilization and knowledge discovery is continuously needed to improve the online learning systems.