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Solving mathematical word problems (MWPs) automatically is challenging,primarily due to the semantic gap between human-readable words andmachine-understandable logics. Despite the long history dated back to the1960s,MWPs have regained intensive attention in the past few years with theadvancement of Artificial Intelligence (AI). Solving MWPs successfully isconsidered as a milestone towards general AI. Many systems have claimedpromising results in self-crafted and small-scale datasets. However, whenapplied on large and diverse datasets, none of the proposed methods in theliterature achieves high precision, revealing that current MWP solvers stillhave much room for improvement. This motivated us to present a comprehensivesurvey to deliver a clear and complete picture of automatic math problemsolvers. In this survey, we emphasize on algebraic word problems, summarizetheir extracted features and proposed techniques to bridge the semantic gap andcompare their performance in the publicly accessible datasets. We also coverautomatic solvers for other types of math problems such as geometric problemsthat require the understanding of diagrams. Finally, we identify severalemerging research directions for the readers with interests in MWPs.

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