Publication Type

Journal Article

Version

acceptedVersion

Publication Date

9-2020

Abstract

Solving mathematical word problems (MWPs) automatically is challenging, primarily due to the semantic gap between human-readable words and machine-understandable logics. Despite the long history dated back to the 1960s, MWPs have regained intensive attention in the past few years with the advancement of Artificial Intelligence (AI). Solving MWPs successfully is considered as a milestone towards general AI. Many systems have claimed promising results in self-crafted and small-scale datasets. However, when applied on large and diverse datasets, none of the proposed methods in the literature achieves high precision, revealing that current MWP solvers still have much room for improvement. This motivated us to present a comprehensive survey to deliver a clear and complete picture of automatic math problem solvers. In this survey, we emphasize on algebraic word problems, summarize their extracted features and proposed techniques to bridge the semantic gap, and compare their performance in the publicly accessible datasets. We also cover automatic solvers for other types of math problems such as geometric problems that require the understanding of diagrams. Finally, we identify several emerging research directions for the readers with interests in MWPs.

Keywords

Semantics, Feature extraction, Mathematical model, Cognition, Natural languages, Deep learning

Discipline

Databases and Information Systems

Research Areas

Information Systems and Management

Publication

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volume

42

Issue

9

First Page

2287

Last Page

2305

ISSN

0162-8828

Identifier

10.1109/TPAMI.2019.2914054

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TPAMI.2019.2914054

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