@InProceedings{ling-EtAl:2017:Long,
  author    = {Ling, Wang  and  Yogatama, Dani  and  Dyer, Chris  and  Blunsom, Phil},
  title     = {Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {158--167},
  abstract  = {Solving algebraic word problems requires executing a series of arithmetic
	operations---a program---to obtain a final answer. However, since programs can
	be arbitrarily complicated, inducing them directly from question-answer pairs
	is a formidable challenge. To make this task more feasible, we solve these
	problems by generating answer rationales, sequences of natural language and
	human-readable mathematical expressions that derive the final answer through a
	series of small steps. Although rationales do not explicitly specify programs,
	they provide a scaffolding for their structure via intermediate milestones. To
	evaluate our approach, we have created a new 100,000-sample dataset of
	questions, answers and rationales. Experimental results show that indirect
	supervision of program learning via answer rationales is a promising strategy
	for inducing arithmetic programs.},
  url       = {http://aclweb.org/anthology/P17-1015}
}