A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving
Qinzhuo Wu, Qi Zhang, Jinlan Fu, Xuanjing Huang · 2020
With the advancements in natural language processing tasks, math word problem solving has received increasing attention.Previous methods have achieved promising results but ignore background common-sense knowledge not directly provided by the problem.In addition, during generation, they focus on local features while neglecting global information.To incorporate external knowledge and global expression information, we propose a novel knowledge-aware sequence-to-tree (KA-S2T) network in which the entities in the problem sequences and their categories are modeled as an entity graph.Based on this entity graph, a graph attention network is used to capture knowledge-aware problem representations.Further, we use a tree-structured decoder with a state aggregation mechanism to capture the long-distance dependency and global expression information.Experimental results on the Math23K dataset revealed that the KA-S2T model can achieve better performance than previously reported best results.