论文标题
用于解决数学单词问题的基于反向操作的数据增强
Reverse Operation based Data Augmentation for Solving Math Word Problems
论文作者
论文摘要
自动解决数学单词问题是自然语言处理领域的关键任务。最近的型号已达到其性能瓶颈，需要更多的高质量数据进行培训。我们提出了一种新颖的数据增强方法，该方法逆转了数学单词问题的数学逻辑，以产生新的高质量数学问题，并引入新的知识点，从而使学习数学推理逻辑有益。我们将增强数据应用于两个SOTA数学单词问题解决模型，并将我们的结果与强大的数据增强基线进行比较。实验结果表明了我们方法的有效性。我们在https://github.com/yiyunya/roda上发布代码和数据。
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.
