  Automatically solving math word problems is a critical task in the field of
natural language processing. Recent models have reached their performance
bottleneck and require more high-quality data for training. We propose a novel
data augmentation method that reverses the mathematical logic of math word
problems to produce new high-quality math problems and introduce new knowledge
points that can benefit learning the mathematical reasoning logic. We apply the
augmented data on two SOTA math word problem solving models and compare our
results with a strong data augmentation baseline. Experimental results show the
effectiveness of our approach. We release our code and data at
https://github.com/yiyunya/RODA.
