Math Word Problem Solving with Explicit Numerical Values

By Qinzhuo Wu, Qi Zhang, Zhongyu Wei, Xuanjing Huang. 2021

62 citations recorded by the source corpus.

In recent years, math word problem solving has received considerable attention and achieved promising results, but previous methods rarely take numerical values into consideration. Most methods treat the numerical values in the problems as number symbols, and ignore the prominent role of the numerical values in solving the problem. In this paper, we propose a novel approach called NumS2T, which enhances math word problem solving performance by explicitly incorporating numerical values into a sequence-to-tree network. In addition, a numerical properties prediction mechanism is used to capture the category and comparison information of numerals and measure their importance in global expressions. Experimental results on the Math23K and APE datasets demonstrate that our model achieves better performance than existing state-of-the-art models.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers (2021, methodology): “, 2021), and solvers using external information and signals (Liang and Zhang; Wu et al., 2021).”
A Survey of Deep Learning for Mathematical Reasoning (2022, unknown): “Recent work (Wu et al., 2021d; Krishna et al., 2021; Ri and Tsuruoka, 2022; Anderson and Farrell, 2022; Wu et al., 2022c) shows that pre-training on data that is fully synthetically generated—synthetic pre-training can actually provide substantial gains.”