Exploring the Benefits of Data Augmentation in Math Word Problem Solving


Yigit G., AMASYALI M. F.

17th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2023, Hammamet, Tunisia, 20 - 23 September 2023 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/inista59065.2023.10310417
  • City: Hammamet
  • Country: Tunisia
  • Keywords: Data Augmentation, Math Word Problems, Question Answering
  • Yıldız Technical University Affiliated: Yes

Abstract

Math Word Problem (MWP) is a challenging Natural Language Processing (NLP) task. Existing MWP solvers have shown that current models need to generalize better and obtain higher performances. In this study, we aim to enrich existing MWP datasets with high-quality data, which may improve MWP solvers' performances. We propose several data augmentation methods by applying minor modifications to the problem texts and equations of English MWPs datasets which contain equations with one unknown. Extensive experiments on two MWPs datasets have shown that data created by augmented methods have considerably improved performance. Moreover, further increasing the training samples by combining the samples generated by the proposed augmentation methods provides further performance improvements.