Tree-structured Decoding for Solving Math Word Problems

Qianying Liu, Wenyv Guan, Sujian Li, Daisuke Kawahara

Abstract
Automatically solving math word problems is an interesting research topic that needs to bridge natural language descriptions and formal math equations. Previous studies introduced end-to-end neural network methods, but these approaches did not efficiently consider an important characteristic of the equation, i.e., an abstract syntax tree. To address this problem, we propose a tree-structured decoding method that generates the abstract syntax tree of the equation in a top-down manner. In addition, our approach can automatically stop during decoding without a redundant stop token. The experimental results show that our method achieves single model state-of-the-art performance on Math23K, which is the largest dataset on this task.
Anthology ID:
D19-1241
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2370–2379
Language:
URL:
https://aclanthology.org/D19-1241
DOI:
10.18653/v1/D19-1241
Bibkey:
Cite (ACL):
Qianying Liu, Wenyv Guan, Sujian Li, and Daisuke Kawahara. 2019. Tree-structured Decoding for Solving Math Word Problems. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2370–2379, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Tree-structured Decoding for Solving Math Word Problems (Liu et al., EMNLP-IJCNLP 2019)
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https://preview.aclanthology.org/emnlp22-frontmatter/D19-1241.pdf
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