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

Qianying Liu

doi:10.1109/TASLP.2021.3126932

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

2022-01-01

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DOI：10.1109/TASLP.2021.3126932

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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).

Keywords:

Mathematical models

Automobiles

Task analysis

Cognition

Knowledge discovery

Speech processing

Reliability

Math word problems

question answering

data augmentation

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

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