@InProceedings{duan-EtAl:2017:EMNLP2017,
  author    = {Duan, Nan  and  Tang, Duyu  and  Chen, Peng  and  Zhou, Ming},
  title     = {Question Generation for Question Answering},
  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     = {866--874},
  abstract  = {This paper presents how to generate questions from given passages using neural
	networks, where large scale QA pairs are automatically crawled and processed
	from Community-QA website, and used as training data. The contribution of the
	paper is 2-fold: First, two types of question generation approaches are
	proposed, one is a retrieval-based method using convolution neural network
	(CNN), the other is a generation-based method using recurrent
	neural network (RNN); Second, we show how to leverage the generated questions
	to improve existing question answering systems. We evaluate our question
	generation method for the answer sentence selection task on three benchmark
	datasets, including SQuAD, MS MARCO, and WikiQA. Experimental results show
	that, by using generated questions as an extra signal, significant QA
	improvement can be achieved.},
  url       = {https://www.aclweb.org/anthology/D17-1090}
}