@InProceedings{wang-liu-shi:2017:EMNLP2017,
  author    = {Wang, Yan  and  Liu, Xiaojiang  and  Shi, Shuming},
  title     = {Deep Neural Solver for Math Word Problems},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {https://www.aclweb.org/anthology/D17-1088}
}