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