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 the1960s,
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.

该研究探讨了自动解决数学问题的挑战，包括语言和逻辑之间的语义鸿沟，综述了将人工智能用于代数和几何问题的主要技术及其性能，并探讨未来的研究方向。

语义解析的瓶颈：自动数学题解决方案的调查

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

We propose a new evaluation for automatic solvers for algebra word problems,
which can identify mistakes that existing evaluations overlook. Our proposal is
to evaluate such solvers using derivations, which reflect how an equation
system was constructed from the word problem. To accomplish this, we develop an
algorithm for checking the equivalence between two derivations, and show how
derivation an- notations can be semi-automatically added to existing datasets.
To make our experiments more comprehensive, we include the derivation
annotation for DRAW-1K, a new dataset containing 1000 general algebra word
problems. In our experiments, we found that the annotated derivations enable a
more accurate evaluation of automatic solvers than previously used metrics. We
release derivation annotations for over 2300 algebra word problems for future
evaluations.

本文提出了一种新的自动求解代数问题评估方法 —— 基于导出式的评估，可以识别现有评估揭示的错误，并通过导出式检查等算法向现有数据集中加入导出式注释，经实验证明，使用此方法的自动求解算法评估更为准确，我们已经发布了 2300 个代数单词问题的导出式注释以供未来评估。