Text2Math: End-to-end Parsing Text into Math Expressions

Yanyan Zou, Wei Lu


Abstract
We propose Text2Math, a model for semantically parsing text into math expressions. The model can be used to solve different math related problems including arithmetic word problems and equation parsing problems. Unlike previous approaches, we tackle the problem from an end-to-end structured prediction perspective where our algorithm aims to predict the complete math expression at once as a tree structure, where minimal manual efforts are involved in the process. Empirical results on benchmark datasets demonstrate the efficacy of our approach.
Anthology ID:
D19-1536
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
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
5327–5337
Language:
URL:
https://aclanthology.org/D19-1536
DOI:
10.18653/v1/D19-1536
Bibkey:
Cite (ACL):
Yanyan Zou and Wei Lu. 2019. Text2Math: End-to-end Parsing Text into Math Expressions. 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 5327–5337, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Text2Math: End-to-end Parsing Text into Math Expressions (Zou & Lu, EMNLP-IJCNLP 2019)
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