Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints
arXiv:2109.04546
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.
arXiv commentEMNLP 2021
References in corpus (8)
- Categorical Reparameterization with Gumbel-Softmax
- Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
- Microsoft COCO Captions: Data Collection and Evaluation Server
- The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
- CTRL: A Conditional Transformer Language Model for Controllable Generation
- GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution
- A Joint Model for Question Answering and Question Generation
- Adversarial Text Generation via Feature-Mover's Distance