Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations

TL;DR

This paper presents an end-to-end neural model that generates diverse mathematical word problems from commonsense knowledge graphs and equations, improving relevance, diversity, and coherence over existing methods.

Contribution

The authors introduce a novel model that fuses symbolic equations and commonsense knowledge for realistic MWP generation, outperforming state-of-the-art models.

Findings

Superior performance on automatic metrics (BLEU-4, ROUGE-L, Self-BLEU)

Outperforms SOTA models in human evaluations of relevance and coherence

Generates diverse MWPs aligned with real-world scenarios

Abstract

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 \url{https://github.com/tal-ai/MaKE_EMNLP2021}.