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

arXiv: https://arxiv.org/abs/1808.07290 · HTML: https://mlsanity.com/paper/1808.07290/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2018.

## Built on by (most cited first, 30 of 31)

- [Are NLP Models really able to Solve Simple Math Word Problems?](https://mlsanity.com/paper/2103.07191/) (2021)
- [A Survey of Deep Learning for Mathematical Reasoning](https://mlsanity.com/paper/2212.10535/) (2022)
- [A Survey on Large Language Models for Mathematical Reasoning](https://mlsanity.com/paper/2506.08446/) (2025)
- [MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers](https://mlsanity.com/paper/2109.00799/) (2021)
- [PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning](https://mlsanity.com/paper/2112.05136/) (2021)
- [Are Deep Neural Networks SMARTer than Second Graders?](https://mlsanity.com/paper/2212.09993/) (2022)
- [GraphReason: Enhancing Reasoning Capabilities of Large Language Models through A Graph-Based Verification Approach](https://mlsanity.com/paper/2308.09267/) (2023)
- [Learning by Analogy: Diverse Questions Generation in Math Word Problem](https://mlsanity.com/paper/2306.09064/) (2023)
- [Using Large Language Model to Solve and Explain Physics Word Problems Approaching Human Level](https://mlsanity.com/paper/2309.08182/) (2023)
- [NumGPT: Improving Numeracy Ability of Generative Pre-trained Models](https://mlsanity.com/paper/2109.03137/) (2021)
- [A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?](https://mlsanity.com/paper/2305.12920/) (2023)
- [Sequence to General Tree: Knowledge-Guided Geometry Word Problem Solving](https://mlsanity.com/paper/2106.00990/) (2021)
- [From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto](https://mlsanity.com/paper/2402.16269/) (2024)
- [A Survey in Mathematical Language Processing](https://mlsanity.com/paper/2205.15231/) (2022)
- [Why are NLP Models Fumbling at Elementary Math? A Survey of Deep Learning based Word Problem Solvers](https://mlsanity.com/paper/2205.15683/) (2022)
- [Non-Autoregressive Math Word Problem Solver with Unified Tree Structure](https://mlsanity.com/paper/2305.04556/) (2023)
- [Math Word Problem Solving by Generating Linguistic Variants of Problem Statements](https://mlsanity.com/paper/2306.13899/) (2023)
- [Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems](https://mlsanity.com/paper/2211.07455/) (2022)
- [Proactive and Reactive Engagement of Artificial Intelligence Methods for Education: A Review](https://mlsanity.com/paper/2301.10231/) (2023)
- [Learning by Applying: A General Framework for Mathematical Reasoning via Enhancing Explicit Knowledge Learning](https://mlsanity.com/paper/2302.05717/) (2023)
- [Analogical Math Word Problems Solving with Enhanced Problem-Solution Association](https://mlsanity.com/paper/2212.00837/) (2022)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [Quantitative Information Extraction from Humanitarian Documents](https://mlsanity.com/paper/2408.04941/) (2024)
- [STEM-POM: Evaluating Language Models Math-Symbol Reasoning in Document Parsing](https://mlsanity.com/paper/2411.00387/) (2024)
- [From Large to Tiny: Distilling and Refining Mathematical Expertise for Math Word Problems with Weakly Supervision](https://mlsanity.com/paper/2403.14390/) (2024)
- [Teaching LLMs for Step-Level Automatic Math Correction via Reinforcement Learning](https://mlsanity.com/paper/2503.18432/) (2025)
- [WARM: A Weakly (+Semi) Supervised Model for Solving Math word Problems](https://mlsanity.com/paper/2104.06722/) (2021)
- [ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision](https://mlsanity.com/paper/2210.07017/) (2022)
- [Foundation of Intelligence: Review of Math Word Problems from Human Cognition Perspective](https://mlsanity.com/paper/2510.21999/) (2025)
- [A Theoretical Review on Solving Algebra Problems](https://mlsanity.com/paper/2411.00031/) (2024)

## Abstract (arXiv)

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.