论文标题
数学单词问题的基于语义的数据增强
Semantic-based Data Augmentation for Math Word Problems
论文作者
论文摘要
神经MWP求解器很难处理局部差异很小。在MWP任务中，一些本地更改保存了原始语义，而其他局部更改可能会完全改变基本逻辑。当前，现有用于MWP任务的数据集包含有限的样本，这对于神经模型学会在问题中解除不同种类的本地差异并正确解决问题的关键。在本文中，我们提出了一系列新型的数据增强方法，以补充现有数据集，并使用具有不同种类的局部差异增强的数据，并有助于提高当前神经模型的概括能力。新样本是由知识指导实体替代品和逻辑指导的问题重组生成的。增强方法可确保保持新数据及其标签之间的一致性。实验结果表明了我们方法的必要性和有效性。
It's hard for neural MWP solvers to deal with tiny local variances. In MWP task, some local changes conserve the original semantic while the others may totally change the underlying logic. Currently, existing datasets for MWP task contain limited samples which are key for neural models to learn to disambiguate different kinds of local variances in questions and solve the questions correctly. In this paper, we propose a set of novel data augmentation approaches to supplement existing datasets with such data that are augmented with different kinds of local variances, and help to improve the generalization ability of current neural models. New samples are generated by knowledge guided entity replacement, and logic guided problem reorganization. The augmentation approaches are ensured to keep the consistency between the new data and their labels. Experimental results have shown the necessity and the effectiveness of our methods.
