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
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
845–854
Language:
URL:
https://www.aclweb.org/anthology/D17-1088
DOI:
10.18653/v1/D17-1088
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1088.pdf