The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers.

Clicks: 399
ID: 96740
2019
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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 $1960$s, 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.
Reference Key
zhang2019theieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhang, Dongxiang;Wang, Lei;Zhang, Luming;Dai, Bing Tian;Shen, Heng Tao;
Journal ieee transactions on pattern analysis and machine intelligence
Year 2019
DOI
10.1109/TPAMI.2019.2914054
URL
Keywords
standardization ontology genetic circuits synthetic biology open language

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