Translating a Math Word Problem to a Expression Tree

Lei Wang, Yan Wang, Deng Cai, Dongxiang Zhang, Xiaojiang Liu


Abstract
Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. Despite its simplicity, a drawback still remains: a math word problem can be correctly solved by more than one equations. This non-deterministic transduction harms the performance of maximum likelihood estimation. In this paper, by considering the uniqueness of expression tree, we propose an equation normalization method to normalize the duplicated equations. Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving. We find that each model has its own specialty in solving problems, consequently an ensemble model is then proposed to combine their advantages. Experiments on dataset Math23K show that the ensemble model with equation normalization significantly outperforms the previous state-of-the-art methods.
Anthology ID:
D18-1132
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1064–1069
Language:
URL:
https://aclanthology.org/D18-1132
DOI:
10.18653/v1/D18-1132
Bibkey:
Cite (ACL):
Lei Wang, Yan Wang, Deng Cai, Dongxiang Zhang, and Xiaojiang Liu. 2018. Translating a Math Word Problem to a Expression Tree. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1064–1069, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Translating a Math Word Problem to a Expression Tree (Wang et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1132.pdf
Data
Math23K