Details
| Original language | English |
|---|---|
| Number of pages | 27 |
| Journal | Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery |
| Early online date | 24 Mar 2024 |
| Publication status | E-pub ahead of print - 24 Mar 2024 |
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.
Keywords
- automated word problem, deep learning, natural language processing, solving
ASJC Scopus subject areas
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In: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 24.03.2024.
Research output: Contribution to journal › Review article › Research › peer review
}
TY - JOUR
T1 - Does a language model “understand” high school math? A survey of deep learning based word problem solvers
AU - Sundaram, Sowmya S.
AU - Gurajada, Sairam
AU - Padmanabhan, Deepak
AU - Abraham, Savitha Sam
AU - Fisichella, Marco
N1 - Funding Information: Open Access funding enabled and organized by Projekt DEAL
PY - 2024/3/24
Y1 - 2024/3/24
N2 - 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.
AB - 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.
KW - automated word problem
KW - deep learning
KW - natural language processing
KW - solving
UR - http://www.scopus.com/inward/record.url?scp=85189518550&partnerID=8YFLogxK
U2 - 10.1002/widm.1534
DO - 10.1002/widm.1534
M3 - Review article
AN - SCOPUS:85189518550
JO - Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
JF - Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
SN - 1942-4787
ER -