@inproceedings{zou-lu-2019-text2math,
title = "{T}ext2{M}ath: End-to-end Parsing Text into Math Expressions",
author = "Zou, Yanyan and
Lu, Wei",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1536",
doi = "10.18653/v1/D19-1536",
pages = "5327--5337",
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.",
}
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%0 Conference Proceedings
%T Text2Math: End-to-end Parsing Text into Math Expressions
%A Zou, Yanyan
%A Lu, Wei
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F zou-lu-2019-text2math
%X 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.
%R 10.18653/v1/D19-1536
%U https://aclanthology.org/D19-1536
%U https://doi.org/10.18653/v1/D19-1536
%P 5327-5337
Markdown (Informal)
[Text2Math: End-to-end Parsing Text into Math Expressions](https://aclanthology.org/D19-1536) (Zou & Lu, EMNLP-IJCNLP 2019)
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.