| Challenge: | Existing generation methods suffer from repeated sub-expression generation and deductive methods are restricted to dealing with binary operations. |
| Approach: | They propose a multivariate directed acyclic graph (mDAG) which generates the topological ordering of mDAg by equipping a generation model with a re-encoder to keep the deductive property but avoid the expensive enumeration of deductive methods. |
| Outcome: | The proposed model performs well on the widely used benchmarks and solves multivariate operators on the CMWPA benchmark. |
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| Challenge: | Existing studies formalize MWP as a generation task but mathematical expressions are prone to minor mistakes. |
| Approach: | They propose a ranking task for math word problem (MWP) that learns from its own mistakes and distinguishes between correct and incorrect expressions. |
| Outcome: | The proposed model outperforms baselines on the classical Math23k dataset and is 7% higher than the state-of-the-art. |
DISK: Domain-constrained Instance Sketch for Math Word Problem Generation (2022.coling-1)
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| Challenge: | Existing methods for generating MWP text from equations are inflexible and require pre-defined templates. |
| Approach: | They propose a neural model which generates MWPs from equations by constructing a Quantity Cell Graph from the retrieved MWp instance and reasoning over it. |
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Graph-to-Tree Learning for Solving Math Word Problems (2020.acl-main)
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| Challenge: | Existing tree-based neural models do not capture the relationships and order information among the quantities well. |
| Approach: | They propose a novel deep learning architecture that combines the merits of the graph-based encoder and tree-based decoder to generate better solution expressions. |
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Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations (2021.emnlp-main)
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| Challenge: | Existing models for generating mathematical word problems are lacking in educational assessment. |
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Non-Autoregressive Math Word Problem Solver with Unified Tree Structure (2023.emnlp-main)
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| Challenge: | Existing MWP solvers do not handle variants that can be derived via mathematical manipulation. |
| Approach: | They propose a non-autoregressive solver to present a solution expression and decode it from a given problem description. |
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Multi-lingual Mathematical Word Problem Generation using Long Short Term Memory Networks with Enhanced Input Features (2020.lrec-1)
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| Challenge: | Existing methods for multi-lingual MWP generation are incapable of identifying language specific constraints, especially in morphologically rich yet low resource languages such as Sinhala and Tamil. |
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Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints (2021.emnlp-main)
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| Challenge: | Existing approaches to generate arithmetic math word problems are invalid or have unsatisfactory language quality. |
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Math Word Problem Solving by Generating Linguistic Variants of Problem Statements (2023.acl-srw)
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Syed Rifat Raiyan, Md Nafis Faiyaz, Shah Md. Jawad Kabir, Mohsinul Kabir, Hasan Mahmud, Md Kamrul Hasan
| Challenge: | Existing models for solving Math Word Problems depend on shallow heuristics and spurious correlations to derive the solution expressions. |
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Sequence to General Tree: Knowledge-Guided Geometry Word Problem Solving (2021.acl-short)
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| Challenge: | Existing neural solvers only generate binary expression trees that contain basic arithmetic operators and do not explicitly use the math formulas. |
| Approach: | They propose a sequence-to-general tree that generates interpretable and executable operation trees where nodes can be formulas with an arbitrary number of arguments. |
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Computational Blueprints: Generating Isomorphic Math Problems with Large Language Models (2025.emnlp-industry)
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| Challenge: | Existing studies on mathematics problem generation focus on data augmentation rather than direct educational deployment. |
| Approach: | They propose a task to generate structurally consistent variants of source problems . they also propose 'Computational Blueprints for Isomorphic Twins' |
| Outcome: | The proposed task produces structurally consistent variants of source problems . the authors show that it is superior on generation accuracy and cost-effectiveness at scale . |