Semantic-based Data Augmentation for Math Word Problems

TL;DR

This paper introduces semantic-based data augmentation techniques for math word problems to enhance neural model generalization by generating diverse, semantically consistent problem variations.

Contribution

It proposes novel knowledge and logic-guided augmentation methods that produce diverse training samples while preserving problem semantics and correctness.

Findings

Augmentation improves neural model accuracy on MWP tasks.

Generated data increases dataset diversity and robustness.

Methods are validated through experimental results.

Abstract

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.