Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints

TL;DR

This paper introduces a new method for generating math word problems that ensures mathematical correctness and contextual relevance, using pre-trained language models and consistency constraints to improve quality and validity.

Contribution

The paper presents a novel approach combining language models and equation constraints for more accurate and diverse math word problem generation.

Findings

Outperforms existing methods in language quality and mathematical validity

Achieves higher diversity in generated MWPs

Demonstrates effectiveness across multiple datasets

Abstract

We study the problem of generating arithmetic math word problems (MWPs) given a math equation that specifies the mathematical computation and a context that specifies the problem scenario. Existing approaches are prone to generating MWPs that are either mathematically invalid or have unsatisfactory language quality. They also either ignore the context or require manual specification of a problem template, which compromises the diversity of the generated MWPs. In this paper, we develop a novel MWP generation approach that leverages i) pre-trained language models and a context keyword selection model to improve the language quality of the generated MWPs and ii) an equation consistency constraint for math equations to improve the mathematical validity of the generated MWPs. Extensive quantitative and qualitative experiments on three real-world MWP datasets demonstrate the superior performance of our approach compared to various baselines.