# Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations

arXiv: https://arxiv.org/abs/2010.06196 · HTML: https://mlsanity.com/paper/2010.06196/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 16 of 16)

- [Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations](https://mlsanity.com/paper/2302.06885/) (2023)
- [Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints](https://mlsanity.com/paper/2109.04546/) (2021)
- [Learning by Analogy: Diverse Questions Generation in Math Word Problem](https://mlsanity.com/paper/2306.09064/) (2023)
- [ChatGPT as a Math Questioner? Evaluating ChatGPT on Generating Pre-university Math Questions](https://mlsanity.com/paper/2312.01661/) (2023)
- [MultiQG-TI: Towards Question Generation from Multi-modal Sources](https://mlsanity.com/paper/2307.04643/) (2023)
- [Diversifying Question Generation over Knowledge Base via External Natural Questions](https://mlsanity.com/paper/2309.14362/) (2023)
- [Proactive and Reactive Engagement of Artificial Intelligence Methods for Education: A Review](https://mlsanity.com/paper/2301.10231/) (2023)
- [Synthetic Context Generation for Question Generation](https://mlsanity.com/paper/2406.13188/) (2024)
- [Automatic Generation of Word Problems for Academic Education via Natural Language Processing (NLP)](https://mlsanity.com/paper/2109.13123/) (2021)
- [From Objectives to Questions: A Planning-based Framework for Educational Mathematical Question Generation](https://mlsanity.com/paper/2506.00963/) (2025)
- [Transparent AI for Mathematics: Transformer-Based Large Language Models for Mathematical Entity Relationship Extraction with XAI](https://mlsanity.com/paper/2603.06348/) (2026)
- [DISK: Domain-constrained Instance Sketch for Math Word Problem Generation](https://mlsanity.com/paper/2204.04686/) (2022)
- [EduNLP: Towards a Unified and Modularized Library for Educational Resources](https://mlsanity.com/paper/2406.01276/) (2024)
- [Towards Generating Controllable and Solvable Geometry Problem by Leveraging Symbolic Deduction Engine](https://mlsanity.com/paper/2506.02565/) (2025)
- [Computational Blueprints: Generating Isomorphic Mathematics Problems with Large Language Models](https://mlsanity.com/paper/2511.07932/) (2025)
- [A self-evolving multi-role collaborative framework with fine-grained difficulty guidance for innovative mathematical problem generation](https://mlsanity.com/paper/2601.11792/) (2026)

## Abstract (arXiv)

There is an increasing interest in the use of mathematical word problem (MWP) generation in educational assessment. Different from standard natural question generation, MWP generation needs to maintain the underlying mathematical operations between quantities and variables, while at the same time ensuring the relevance between the output and the given topic. To address above problem, we develop an end-to-end neural model to generate diverse MWPs in real-world scenarios from commonsense knowledge graph and equations. The proposed model (1) learns both representations from edge-enhanced Levi graphs of symbolic equations and commonsense knowledge; (2) automatically fuses equation and commonsense knowledge information via a self-planning module when generating the MWPs. Experiments on an educational gold-standard set and a large-scale generated MWP set show that our approach is superior on the MWP generation task, and it outperforms the SOTA models in terms of both automatic evaluation metrics, i.e., BLEU-4, ROUGE-L, Self-BLEU, and human evaluation metrics, i.e., equation relevance, topic relevance, and language coherence. To encourage reproducible results, we make our code and MWP dataset public available at urlhttps://github.com/tal-ai/MaKEEMNLP2021.