Semantic-based Data Augmentation for Math Word Problems
This work addresses a domain-specific issue for neural models in math education by providing incremental improvements through enhanced datasets.
The paper tackles the problem of neural math word problem solvers struggling with small local variations by proposing semantic-based data augmentation methods, which improved model generalization as shown in experimental results.
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.