DISK: Domain-constrained Instance Sketch for Math Word Problem Generation
Tianyang Cao, Shuang Zeng, Xiaodan Xu, Mairgup Mansur, Baobao Chang

TL;DR
This paper introduces a neural model for generating math word problems from equations, leveraging domain knowledge, instance retrieval, and graph reasoning to produce coherent and contextually accurate problems.
Contribution
It presents a novel approach combining domain-conditioned retrieval and graph-based reasoning to improve math word problem generation from equations.
Findings
Achieves superior performance on automatic evaluation metrics.
Outperforms baseline models in human evaluations.
Enhances understanding of real-world scenarios through graph reasoning.
Abstract
A math word problem (MWP) is a coherent narrative which reflects the underlying logic of math equations. Successful MWP generation can automate the writing of mathematics questions. Previous methods mainly generate MWP text based on inflexible pre-defined templates. In this paper, we propose a neural model for generating MWP text from math equations. Firstly, we incorporate a matching model conditioned on the domain knowledge to retrieve a MWP instance which is most consistent with the ground-truth, where the domain is a latent variable extracted with a domain summarizer. Secondly, by constructing a Quantity Cell Graph (QCG) from the retrieved MWP instance and reasoning over it, we improve the model's comprehension of real-world scenarios and derive a domain-constrained instance sketch to guide the generation. Besides, the QCG also interacts with the equation encoder to enhance the…
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Taxonomy
TopicsIntelligent Tutoring Systems and Adaptive Learning · Topic Modeling · Mathematics, Computing, and Information Processing
