Reverse Operation based Data Augmentation for Solving Math Word Problems
This addresses the need for more high-quality training data in natural language processing for math problem-solving, but it is incremental as it builds on existing models and data augmentation techniques.
The paper tackles the performance bottleneck in automatically solving math word problems by proposing a data augmentation method that reverses mathematical logic to generate new problems, showing effectiveness when applied to two state-of-the-art models compared to a baseline.
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.