SMART: A Situation Model for Algebra Story Problems via Attributed Grammar

Authors

  • Yining Hong University of California, Los Angeles
  • Qing Li University of California, Los Angeles
  • Ran Gong University of California, Los Angeles
  • Daniel Ciao University of California, Los Angeles
  • Siyuan Huang University of California, Los Angeles
  • Song-Chun Zhu University of California, Los Angeles

Keywords:

Applications, Neuro-Symbolic AI (NSAI), Education, Question Answering

Abstract

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 situation model, which originates from psychology studies to represent the mental states of humans in problem-solving, and propose 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 further improve the performance of SMART. To study this task more rigorously, we carefully curate a new dataset named 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.

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Published

2021-05-18

How to Cite

Hong, Y., Li, Q., Gong, R., Ciao, D., Huang, S., & Zhu, S.-C. (2021). SMART: A Situation Model for Algebra Story Problems via Attributed Grammar. Proceedings of the AAAI Conference on Artificial Intelligence, 35(14), 13009-13017. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17538

Issue

Section

AAAI Technical Track on Speech and Natural Language Processing I