Deep Neural Solver for Math Word Problems

Yan Wang, Xiaojiang Liu, Shuming Shi


Abstract
This paper presents a deep neural solver to automatically solve math word problems. In contrast to previous statistical learning approaches, we directly translate math word problems to equation templates using a recurrent neural network (RNN) model, without sophisticated feature engineering. We further design a hybrid model that combines the RNN model and a similarity-based retrieval model to achieve additional performance improvement. Experiments conducted on a large dataset show that the RNN model and the hybrid model significantly outperform state-of-the-art statistical learning methods for math word problem solving.
Anthology ID:
D17-1088
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Martha Palmer, Rebecca Hwa, Sebastian Riedel
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
845–854
Language:
URL:
https://aclanthology.org/D17-1088
DOI:
10.18653/v1/D17-1088
Bibkey:
Cite (ACL):
Yan Wang, Xiaojiang Liu, and Shuming Shi. 2017. Deep Neural Solver for Math Word Problems. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 845–854, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Deep Neural Solver for Math Word Problems (Wang et al., EMNLP 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-bitext-workshop/D17-1088.pdf
Data
Math23KALG514