@inproceedings{wang-etal-2017-deep,
title = "Deep Neural Solver for Math Word Problems",
author = "Wang, Yan and
Liu, Xiaojiang and
Shi, Shuming",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1088",
doi = "10.18653/v1/D17-1088",
pages = "845--854",
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.",
}
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%0 Conference Proceedings
%T Deep Neural Solver for Math Word Problems
%A Wang, Yan
%A Liu, Xiaojiang
%A Shi, Shuming
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F wang-etal-2017-deep
%X 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.
%R 10.18653/v1/D17-1088
%U https://aclanthology.org/D17-1088
%U https://doi.org/10.18653/v1/D17-1088
%P 845-854
Markdown (Informal)
[Deep Neural Solver for Math Word Problems](https://aclanthology.org/D17-1088) (Wang et al., EMNLP 2017)
ACL
- Yan Wang, Xiaojiang Liu, and Shuming Shi. 2017. Deep Neural Solver for Math Word Problems. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 845–854, Copenhagen, Denmark. Association for Computational Linguistics.