  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.
