RODA: Reverse Operation Based Data Augmentation for Solving Math Word Problems

Qianying Liu, Wenyu Guan, Sujian Li, Fei Cheng, Daisuke Kawahara, Sadao Kurohashi

Research output: Contribution to journalArticlepeer-review

Abstract

Automatically solving math word problems is a critical task in the field of natural language processing. Recent models have reached their performance bottleneck and require more high-quality data for training. We propose a novel data augmentation method that reverses the mathematical logic of math word problems to produce new high-quality math problems and introduce new knowledge points that can benefit learning the mathematical reasoning logic. We apply the augmented data on two SOTA math word problem solving models and compare our results with a strong data augmentation baseline. Experimental results show the effectiveness of our approach (we release our code and data at https://github.com/yiyunya/RODA).

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Volume30
DOIs
Publication statusPublished - 2022
Externally publishedYes

Keywords

  • data augmentation
  • Math word problems
  • question answering

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Acoustics and Ultrasonics
  • Computational Mathematics
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'RODA: Reverse Operation Based Data Augmentation for Solving Math Word Problems'. Together they form a unique fingerprint.

Cite this