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

arXiv: https://arxiv.org/abs/2012.14011 · HTML: https://mlsanity.com/paper/2012.14011/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2020.

## Built on by (most cited first, 14 of 14)

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- [Generalizing Math Word Problem Solvers via Solution Diversification](https://mlsanity.com/paper/2212.00833/) (2022)
- [Analogical Math Word Problems Solving with Enhanced Problem-Solution Association](https://mlsanity.com/paper/2212.00837/) (2022)
- [Improving Compositional Generalization in Math Word Problem Solving](https://mlsanity.com/paper/2209.01352/) (2022)
- [Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction](https://mlsanity.com/paper/2205.02834/) (2022)

## Abstract (arXiv)

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 improve the performance of SMART further. To rigorously study this task, 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.