@InProceedings{huang-EtAl:2017:EMNLP20171,
  author    = {Huang, Danqing  and  Shi, Shuming  and  Lin, Chin-Yew  and  Yin, Jian},
  title     = {Learning Fine-Grained Expressions to Solve 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     = {805--814},
  abstract  = {This paper presents a novel template-based method to solve math word problems.
	This method learns the mappings between math concept phrases in math word
	problems and their math expressions from training data. For each equation
	template, we automatically construct a rich template sketch by aggregating
	information from various problems with the same template. Our approach is
	implemented in a two-stage system. It first retrieves a few relevant equation
	system templates and aligns numbers in math word problems to those templates
	for candidate equation generation. It then does a fine-grained inference to
	obtain the final answer. Experiment results show that our method achieves an
	accuracy of 28.4% on the linear Dolphin18K benchmark, which is 10% (54%
	relative) higher than previous state-of-the-art systems while achieving an
	accuracy
	increase of 12% (59% relative) on the TS6 benchmark subset.},
  url       = {https://www.aclweb.org/anthology/D17-1084}
}