  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.
