Does a language model “understand” high school math? A survey of deep learning based word problem solvers

Sowmya S. Sundaram*, Sairam Gurajada, Deepak Padmanabhan, Savitha Sam Abraham, Marco Fisichella

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

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Abstract

From the latter half of the last decade, there has been a growing interest in developing algorithms for automatically solving mathematical word problems (MWP). It is a challenging and unique task that demands blending surface level text pattern recognition with mathematical reasoning. In spite of extensive research, we still have a lot to explore for building robust representations of elementary math word problems and effective solutions for the general task. In this paper, we critically examine the various models that have been developed for solving word problems, their pros and cons and the challenges ahead. In the last 2 years, a lot of deep learning models have recorded competing results on benchmark datasets, making a critical and conceptual analysis of literature highly useful at this juncture. We take a step back and analyze why, in spite of this abundance in scholarly interest, the predominantly used experiment and dataset designs continue to be a stumbling block. From the vantage point of having analyzed the literature closely, we also endeavor to provide a road‐map for future math word problem research. This article is categorized under: Technologies > Machine Learning Technologies > Artificial Intelligence Fundamental Concepts of Data and Knowledge > Knowledge Representation.
Original languageEnglish
Article numbere1534
Number of pages27
JournalWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Early online date24 Mar 2024
DOIs
Publication statusEarly online date - 24 Mar 2024

Keywords

  • solving
  • automated word problem
  • natural language processing
  • deep learning

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