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Recent years have seen significant advancement in text generation tasks withthe help of neural language models. However, there exists a challenging task:generating math problem text based on mathematical equations, which has madelittle progress so far. In this paper, we present a novel equation-to-problemtext generation model. In our model, 1) we propose a flexible scheme toeffectively encode math equations, we then enhance the equation encoder by aVaritional Autoen-coder (VAE) 2) given a math equation, we perform topicselection, followed by which a dynamic topic memory mechanism is introduced torestrict the topic distribution of the generator 3) to avoid commonsenseviolation in traditional generation model, we pretrain word embedding withbackground knowledge graph (KG), and we link decoded words to related words inKG, targeted at injecting background knowledge into our model. We evaluate ourmodel through both automatic metrices and human evaluation, experimentsdemonstrate our model outperforms baseline and previous models in both accuracyand richness of generated problem text.

Generating Math Word Problems from Equations with Topic Controlling and Commonsense Enforcement