RODA: Reverse Operation Based Data Augmentation for Solving Math Word Problems

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RODA: Reverse Operation Based Data Augmentation for Solving Math Word Problems

Qianying Liu; Wenyu Guan; Sujian Li; Fei Cheng; Daisuke Kawahara; Sadao Kurohashi

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 ).

Solving Math Word Problems (MWPs) is the task that infers a mathematical expression and the final answer from the natural language description of a math problem, which has been crucial due to its importance of numerically reasoning in natural language (NLP) processing [1][2]Fig. 1 shows two examples which include math word problems and their corresponding solution equations and results.

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