@InProceedings{upadhyay-chang:2017:EACLlong,
  author    = {Upadhyay, Shyam  and  Chang, Ming-Wei},
  title     = {Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {494--504},
  abstract  = {We propose a new evaluation for automatic solvers for algebra word problems,
	which can identify mistakes that existing evaluations overlook. Our proposal is
	to evaluate such solvers using derivations, which reflect how an equation
	system was constructed from the word problem. To accomplish this, we develop an
	algorithm for checking the equivalence between two derivations, and show how
	derivation annotations can be semi-automatically added to existing datasets. To
	make our experiments more comprehensive, we include the derivation annotation
	for DRAW-1K, a new dataset containing 1000 general algebra word problems. In
	our experiments, we found that the annotated derivations enable a more accurate
	evaluation of automatic solvers than previously used metrics. We release
	derivation annotations for over 2300 algebra word problems for future
	evaluations.},
  url       = {http://www.aclweb.org/anthology/E17-1047}
}