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

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

研究成果: Article査読

抄録

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).

本文言語English
ページ(範囲)1-11
ページ数11
ジャーナルIEEE/ACM Transactions on Audio Speech and Language Processing
30
DOI
出版ステータスPublished - 2022
外部発表はい

ASJC Scopus subject areas

  • コンピュータ サイエンス(その他)
  • 音響学および超音波学
  • 計算数学
  • 電子工学および電気工学

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