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Automatically solving math word problems is a critical task in the field ofnatural language processing. Recent models have reached their performancebottleneck and require more high-quality data for training. We propose a noveldata augmentation method that reverses the mathematical logic of math wordproblems to produce new high-quality math problems and introduce new knowledgepoints that can benefit learning the mathematical reasoning logic. We apply theaugmented data on two SOTA math word problem solving models and compare ourresults with a strong data augmentation baseline. Experimental results show theeffectiveness of our approach. We release our code and data athttps://github.com/yiyunya/RODA.

Reverse Operation based Data Augmentation for Solving Math Word Problems