Solving algebra story problems remains a challenging task in artificial
intelligence, which requires a detailed understanding of real-world situations
and a strong mathematical reasoning capability. Previous neural solvers of math
word problems directly translate problem texts into equations, lacking an
explicit interpretation of the situations, and often fail to handle more
sophisticated situations. To address such limits of neural solvers, we
introduce the concept of a \emph{situation model}, which originates from
psychology studies to represent the mental states of humans in problem-solving,
and propose \emph{SMART}, which adopts attributed grammar as the representation
of situation models for algebra story problems. Specifically, we first train an
information extraction module to extract nodes, attributes, and relations from
problem texts and then generate a parse graph based on a pre-defined attributed
grammar. An iterative learning strategy is also proposed to improve the
performance of SMART further. To rigorously study this task, we carefully
curate a new dataset named \emph{ASP6.6k}. Experimental results on ASP6.6k show
that the proposed model outperforms all previous neural solvers by a large
margin while preserving much better interpretability. To test these models'
generalization capability, we also design an out-of-distribution (OOD)
evaluation, in which problems are more complex than those in the training set.
Our model exceeds state-of-the-art models by 17\% in the OOD evaluation,
demonstrating its superior generalization ability.

本文介绍了一种采用属性语法表示情景模型的代数故事问题求解器（SMART），它通过信息提取模块从问题文本中提取节点、属性和关系，然后根据预定义的属性语法生成解析图，并提出了一种迭代学习策略，以进一步提高模型的性能，并通过严格研究此任务来改进模型在此任务上的性能。我们的模型在较复杂的问题上也表现出了优异的泛化能力。