  There is an increasing interest in the use of mathematical word problem (MWP)
generation in educational assessment. Different from standard natural question
generation, MWP generation needs to maintain the underlying mathematical
operations between quantities and variables, while at the same time ensuring
the relevance between the output and the given topic. To address above problem,
we develop an end-to-end neural model to generate diverse MWPs in real-world
scenarios from commonsense knowledge graph and equations. The proposed model
(1) learns both representations from edge-enhanced Levi graphs of symbolic
equations and commonsense knowledge; (2) automatically fuses equation and
commonsense knowledge information via a self-planning module when generating
the MWPs. Experiments on an educational gold-standard set and a large-scale
generated MWP set show that our approach is superior on the MWP generation
task, and it outperforms the SOTA models in terms of both automatic evaluation
metrics, i.e., BLEU-4, ROUGE-L, Self-BLEU, and human evaluation metrics, i.e.,
equation relevance, topic relevance, and language coherence. To encourage
reproducible results, we make our code and MWP dataset public available at
\url{https://github.com/tal-ai/MaKE_EMNLP2021}.
