The gap of semantic parsing: A survey on automatic Math word problem solvers
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. |