Towards Generating Math Word Problems from Equations and Topics

Qingyu Zhou, Danqing Huang


Abstract
A math word problem is a narrative with a specific topic that provides clues to the correct equation with numerical quantities and variables therein. In this paper, we focus on the task of generating math word problems. Previous works are mainly template-based with pre-defined rules. We propose a novel neural network model to generate math word problems from the given equations and topics. First, we design a fusion mechanism to incorporate the information of both equations and topics. Second, an entity-enforced loss is introduced to ensure the relevance between the generated math problem and the equation. Automatic evaluation results show that the proposed model significantly outperforms the baseline models. In human evaluations, the math word problems generated by our model are rated as being more relevant (in terms of solvability of the given equations and relevance to topics) and natural (i.e., grammaticality, fluency) than the baseline models.
Anthology ID:
W19-8661
Volume:
Proceedings of the 12th International Conference on Natural Language Generation
Month:
October–November
Year:
2019
Address:
Tokyo, Japan
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
494–503
Language:
URL:
https://aclanthology.org/W19-8661
DOI:
10.18653/v1/W19-8661
Bibkey:
Cite (ACL):
Qingyu Zhou and Danqing Huang. 2019. Towards Generating Math Word Problems from Equations and Topics. In Proceedings of the 12th International Conference on Natural Language Generation, pages 494–503, Tokyo, Japan. Association for Computational Linguistics.
Cite (Informal):
Towards Generating Math Word Problems from Equations and Topics (Zhou & Huang, INLG 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/W19-8661.pdf
Supplementary attachment:
 W19-8661.Supplementary_Attachment.pdf